feat: 实现TF-IDF特征提取器和逻辑回归模型用于语义兴趣评分

- 新增了TfidfFeatureExtractor，用于字符级n-gram的TF-IDF向量化，适用于中文及多语言场景。
- 基于逻辑回归开发了语义兴趣模型，用于多类别兴趣标签（-1、0、1）的预测。
- 创建了在线推理的运行时评分器，实现消息兴趣评分的快速评估。
建立了模型训练、评估和数据集准备的全流程培训体系。
- 集成模型管理，支持热加载与个性化模型选择。

src/chat/semantic_interest/__init__.py

@@ -0,0 +1,30 @@
+"""语义兴趣度计算模块
+
+基于 TF-IDF + Logistic Regression 的语义兴趣度计算系统
+支持人设感知的自动训练和模型切换
+"""
+
+from .auto_trainer import AutoTrainer, get_auto_trainer
+from .dataset import DatasetGenerator, generate_training_dataset
+from .features_tfidf import TfidfFeatureExtractor
+from .model_lr import SemanticInterestModel, train_semantic_model
+from .runtime_scorer import ModelManager, SemanticInterestScorer
+from .trainer import SemanticInterestTrainer
+
+__all__ = [
+    # 运行时评分
+    "SemanticInterestScorer",
+    "ModelManager",
+    # 训练组件
+    "TfidfFeatureExtractor",
+    "SemanticInterestModel",
+    "train_semantic_model",
+    # 数据集生成
+    "DatasetGenerator",
+    "generate_training_dataset",
+    # 训练器
+    "SemanticInterestTrainer",
+    # 自动训练
+    "AutoTrainer",
+    "get_auto_trainer",
+]

src/chat/semantic_interest/auto_trainer.py

@@ -0,0 +1,360 @@
+"""自动训练调度器
+
+监控人设变化，自动触发模型训练和切换
+"""
+
+import asyncio
+import hashlib
+import json
+from datetime import datetime, timedelta
+from pathlib import Path
+from typing import Any
+
+from src.common.logger import get_logger
+from src.config.config import global_config
+from src.chat.semantic_interest.trainer import SemanticInterestTrainer
+
+logger = get_logger("semantic_interest.auto_trainer")
+
+
+class AutoTrainer:
+    """自动训练调度器
+    
+    功能：
+    1. 监控人设变化
+    2. 自动构建训练数据集
+    3. 定期重新训练模型
+    4. 管理多个人设的模型
+    """
+
+    def __init__(
+        self,
+        data_dir: Path | None = None,
+        model_dir: Path | None = None,
+        min_train_interval_hours: int = 24,  # 最小训练间隔（小时）
+        min_samples_for_training: int = 100,  # 最小训练样本数
+    ):
+        """初始化自动训练器
+        
+        Args:
+            data_dir: 数据集目录
+            model_dir: 模型目录
+            min_train_interval_hours: 最小训练间隔（小时）
+            min_samples_for_training: 触发训练的最小样本数
+        """
+        self.data_dir = Path(data_dir or "data/semantic_interest/datasets")
+        self.model_dir = Path(model_dir or "data/semantic_interest/models")
+        self.min_train_interval = timedelta(hours=min_train_interval_hours)
+        self.min_samples = min_samples_for_training
+
+        # 人设状态缓存
+        self.persona_cache_file = self.data_dir / "persona_cache.json"
+        self.last_persona_hash: str | None = None
+        self.last_train_time: datetime | None = None
+
+        # 训练器实例
+        self.trainer = SemanticInterestTrainer(
+            data_dir=self.data_dir,
+            model_dir=self.model_dir,
+        )
+
+        # 确保目录存在
+        self.data_dir.mkdir(parents=True, exist_ok=True)
+        self.model_dir.mkdir(parents=True, exist_ok=True)
+
+        # 加载缓存的人设状态
+        self._load_persona_cache()
+
+        logger.info("[自动训练器] 初始化完成")
+        logger.info(f"  - 数据目录: {self.data_dir}")
+        logger.info(f"  - 模型目录: {self.model_dir}")
+        logger.info(f"  - 最小训练间隔: {min_train_interval_hours}小时")
+
+    def _load_persona_cache(self):
+        """加载缓存的人设状态"""
+        if self.persona_cache_file.exists():
+            try:
+                with open(self.persona_cache_file, "r", encoding="utf-8") as f:
+                    cache = json.load(f)
+                    self.last_persona_hash = cache.get("persona_hash")
+                    last_train_str = cache.get("last_train_time")
+                    if last_train_str:
+                        self.last_train_time = datetime.fromisoformat(last_train_str)
+                logger.info(f"[自动训练器] 加载人设缓存: hash={self.last_persona_hash[:8] if self.last_persona_hash else 'None'}")
+            except Exception as e:
+                logger.warning(f"[自动训练器] 加载人设缓存失败: {e}")
+
+    def _save_persona_cache(self, persona_hash: str):
+        """保存人设状态到缓存"""
+        cache = {
+            "persona_hash": persona_hash,
+            "last_train_time": datetime.now().isoformat(),
+        }
+        try:
+            with open(self.persona_cache_file, "w", encoding="utf-8") as f:
+                json.dump(cache, f, ensure_ascii=False, indent=2)
+            logger.debug(f"[自动训练器] 保存人设缓存: hash={persona_hash[:8]}")
+        except Exception as e:
+            logger.error(f"[自动训练器] 保存人设缓存失败: {e}")
+
+    def _calculate_persona_hash(self, persona_info: dict[str, Any]) -> str:
+        """计算人设信息的哈希值
+        
+        Args:
+            persona_info: 人设信息字典
+            
+        Returns:
+            SHA256 哈希值
+        """
+        # 只关注影响模型的关键字段
+        key_fields = {
+            "name": persona_info.get("name", ""),
+            "interests": sorted(persona_info.get("interests", [])),
+            "dislikes": sorted(persona_info.get("dislikes", [])),
+            "personality": persona_info.get("personality", ""),
+        }
+        
+        # 转为JSON并计算哈希
+        json_str = json.dumps(key_fields, sort_keys=True, ensure_ascii=False)
+        return hashlib.sha256(json_str.encode()).hexdigest()
+
+    def check_persona_changed(self, persona_info: dict[str, Any]) -> bool:
+        """检查人设是否发生变化
+        
+        Args:
+            persona_info: 当前人设信息
+            
+        Returns:
+            True 如果人设发生变化
+        """
+        current_hash = self._calculate_persona_hash(persona_info)
+        
+        if self.last_persona_hash is None:
+            logger.info("[自动训练器] 首次检测人设")
+            return True
+            
+        if current_hash != self.last_persona_hash:
+            logger.info(f"[自动训练器] 检测到人设变化")
+            logger.info(f"  - 旧哈希: {self.last_persona_hash[:8]}")
+            logger.info(f"  - 新哈希: {current_hash[:8]}")
+            return True
+            
+        return False
+
+    def should_train(self, persona_info: dict[str, Any], force: bool = False) -> tuple[bool, str]:
+        """判断是否应该训练模型
+        
+        Args:
+            persona_info: 人设信息
+            force: 强制训练
+            
+        Returns:
+            (是否应该训练, 原因说明)
+        """
+        # 强制训练
+        if force:
+            return True, "强制训练"
+
+        # 检查人设是否变化
+        persona_changed = self.check_persona_changed(persona_info)
+        if persona_changed:
+            return True, "人设发生变化"
+
+        # 检查训练间隔
+        if self.last_train_time is None:
+            return True, "从未训练过"
+
+        time_since_last_train = datetime.now() - self.last_train_time
+        if time_since_last_train >= self.min_train_interval:
+            return True, f"距上次训练已{time_since_last_train.total_seconds() / 3600:.1f}小时"
+
+        return False, "无需训练"
+
+    async def auto_train_if_needed(
+        self,
+        persona_info: dict[str, Any],
+        days: int = 7,
+        max_samples: int = 500,
+        force: bool = False,
+    ) -> tuple[bool, Path | None]:
+        """自动训练（如果需要）
+        
+        Args:
+            persona_info: 人设信息
+            days: 采样天数
+            max_samples: 最大采样数
+            force: 强制训练
+            
+        Returns:
+            (是否训练了, 模型路径)
+        """
+        # 检查是否需要训练
+        should_train, reason = self.should_train(persona_info, force)
+        
+        if not should_train:
+            logger.debug(f"[自动训练器] {reason}，跳过训练")
+            return False, None
+
+        logger.info(f"[自动训练器] 开始自动训练: {reason}")
+
+        try:
+            # 计算人设哈希作为版本标识
+            persona_hash = self._calculate_persona_hash(persona_info)
+            model_version = f"auto_{persona_hash[:8]}_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
+
+            # 执行训练
+            dataset_path, model_path, metrics = await self.trainer.full_training_pipeline(
+                persona_info=persona_info,
+                days=days,
+                max_samples=max_samples,
+                model_version=model_version,
+                tfidf_config={
+                    "analyzer": "char",
+                    "ngram_range": (2, 4),
+                    "max_features": 15000,
+                    "min_df": 3,
+                },
+                model_config={
+                    "class_weight": "balanced",
+                    "max_iter": 1000,
+                },
+            )
+
+            # 更新缓存
+            self.last_persona_hash = persona_hash
+            self.last_train_time = datetime.now()
+            self._save_persona_cache(persona_hash)
+
+            # 创建"latest"符号链接
+            self._create_latest_link(model_path)
+
+            logger.info(f"[自动训练器] 训练完成!")
+            logger.info(f"  - 模型: {model_path.name}")
+            logger.info(f"  - 准确率: {metrics.get('test_accuracy', 0):.4f}")
+
+            return True, model_path
+
+        except Exception as e:
+            logger.error(f"[自动训练器] 训练失败: {e}")
+            import traceback
+            traceback.print_exc()
+            return False, None
+
+    def _create_latest_link(self, model_path: Path):
+        """创建指向最新模型的符号链接
+        
+        Args:
+            model_path: 模型文件路径
+        """
+        latest_path = self.model_dir / "semantic_interest_latest.pkl"
+        
+        try:
+            # 删除旧链接
+            if latest_path.exists() or latest_path.is_symlink():
+                latest_path.unlink()
+            
+            # 创建新链接（Windows 需要管理员权限，使用复制代替）
+            import shutil
+            shutil.copy2(model_path, latest_path)
+            
+            logger.info(f"[自动训练器] 已更新 latest 模型")
+            
+        except Exception as e:
+            logger.warning(f"[自动训练器] 创建 latest 链接失败: {e}")
+
+    async def scheduled_train(
+        self,
+        persona_info: dict[str, Any],
+        interval_hours: int = 24,
+    ):
+        """定时训练任务
+        
+        Args:
+            persona_info: 人设信息
+            interval_hours: 检查间隔（小时）
+        """
+        logger.info(f"[自动训练器] 启动定时训练任务，间隔: {interval_hours}小时")
+
+        while True:
+            try:
+                # 检查并训练
+                trained, model_path = await self.auto_train_if_needed(persona_info)
+                
+                if trained:
+                    logger.info(f"[自动训练器] 定时训练完成: {model_path}")
+                
+                # 等待下次检查
+                await asyncio.sleep(interval_hours * 3600)
+                
+            except Exception as e:
+                logger.error(f"[自动训练器] 定时训练出错: {e}")
+                # 出错后等待较短时间再试
+                await asyncio.sleep(300)  # 5分钟
+
+    def get_model_for_persona(self, persona_info: dict[str, Any]) -> Path | None:
+        """获取当前人设对应的模型
+        
+        Args:
+            persona_info: 人设信息
+            
+        Returns:
+            模型文件路径，如果不存在则返回 None
+        """
+        persona_hash = self._calculate_persona_hash(persona_info)
+        
+        # 查找匹配的模型
+        pattern = f"semantic_interest_auto_{persona_hash[:8]}_*.pkl"
+        matching_models = list(self.model_dir.glob(pattern))
+        
+        if matching_models:
+            # 返回最新的
+            latest = max(matching_models, key=lambda p: p.stat().st_mtime)
+            logger.debug(f"[自动训练器] 找到人设模型: {latest.name}")
+            return latest
+        
+        # 没有找到，返回 latest
+        latest_path = self.model_dir / "semantic_interest_latest.pkl"
+        if latest_path.exists():
+            logger.debug(f"[自动训练器] 使用 latest 模型")
+            return latest_path
+            
+        logger.warning(f"[自动训练器] 未找到可用模型")
+        return None
+
+    def cleanup_old_models(self, keep_count: int = 5):
+        """清理旧模型文件
+        
+        Args:
+            keep_count: 保留最新的 N 个模型
+        """
+        try:
+            # 获取所有自动训练的模型
+            all_models = list(self.model_dir.glob("semantic_interest_auto_*.pkl"))
+            
+            if len(all_models) <= keep_count:
+                return
+            
+            # 按修改时间排序
+            all_models.sort(key=lambda p: p.stat().st_mtime, reverse=True)
+            
+            # 删除旧模型
+            for old_model in all_models[keep_count:]:
+                old_model.unlink()
+                logger.info(f"[自动训练器] 清理旧模型: {old_model.name}")
+                
+            logger.info(f"[自动训练器] 模型清理完成，保留 {keep_count} 个")
+            
+        except Exception as e:
+            logger.error(f"[自动训练器] 清理模型失败: {e}")
+
+
+# 全局单例
+_auto_trainer: AutoTrainer | None = None
+
+
+def get_auto_trainer() -> AutoTrainer:
+    """获取自动训练器单例"""
+    global _auto_trainer
+    if _auto_trainer is None:
+        _auto_trainer = AutoTrainer()
+    return _auto_trainer

src/chat/semantic_interest/dataset.py

@@ -0,0 +1,516 @@
+"""数据集生成与 LLM 标注
+
+从数据库采样消息并使用 LLM 进行兴趣度标注
+"""
+
+import asyncio
+import json
+import random
+from datetime import datetime, timedelta
+from pathlib import Path
+from typing import Any
+
+from src.common.logger import get_logger
+from src.config.config import global_config
+
+logger = get_logger("semantic_interest.dataset")
+
+
+class DatasetGenerator:
+    """训练数据集生成器
+    
+    从历史消息中采样并使用 LLM 进行标注
+    """
+
+    # 标注提示词模板（单条）
+    ANNOTATION_PROMPT = """你是一个帮助标注消息兴趣度的专家。你需要根据人格设定判断该消息是否会引起角色的兴趣。
+
+## 人格信息
+{persona_info}
+
+## 消息内容
+{message_text}
+
+## 标注规则
+请判断角色对这条消息的兴趣程度，返回以下之一：
+- **-1**: 完全不感兴趣或排斥（话题不相关、违背价值观、无聊重复等）
+- **0**: 中立（可以回应但不特别感兴趣）
+- **1**: 感兴趣（话题相关、符合兴趣点、能产生深度对话）
+
+只需返回数字 -1、0 或 1，不要其他内容。"""
+
+    # 批量标注提示词模板
+    BATCH_ANNOTATION_PROMPT = """你是一个帮助标注消息兴趣度的专家。你需要根据人格设定判断每条消息是否会引起角色的兴趣。
+
+## 人格信息
+{persona_info}
+
+## 标注规则
+对每条消息判断角色的兴趣程度：
+- **-1**: 完全不感兴趣或排斥（话题不相关、违背价值观、无聊重复等）
+- **0**: 中立（可以回应但不特别感兴趣）
+- **1**: 感兴趣（话题相关、符合兴趣点、能产生深度对话）
+
+## 消息列表
+{messages_list}
+
+## 输出格式
+请严格按照以下JSON格式返回，每条消息一个标签：
+```json
+{example_output}
+```
+
+只返回JSON，不要其他内容。"""
+
+    def __init__(
+        self,
+        model_name: str | None = None,
+        max_samples_per_batch: int = 50,
+    ):
+        """初始化数据集生成器
+        
+        Args:
+            model_name: LLM 模型名称（None 则使用默认模型）
+            max_samples_per_batch: 每批次最大采样数
+        """
+        self.model_name = model_name
+        self.max_samples_per_batch = max_samples_per_batch
+        self.model_client = None
+
+    async def initialize(self):
+        """初始化 LLM 客户端"""
+        try:
+            from src.llm_models.utils_model import LLMRequest
+            from src.config.config import model_config
+            
+            # 使用 utilities 模型配置（标注更偏工具型）
+            if hasattr(model_config.model_task_config, 'utils'):
+                self.model_client = LLMRequest(
+                    model_set=model_config.model_task_config.utils,
+                    request_type="semantic_annotation"
+                )
+                logger.info(f"数据集生成器初始化完成，使用 utils 模型")
+            else:
+                logger.error("未找到 utils 模型配置")
+                self.model_client = None
+        except ImportError as e:
+            logger.warning(f"无法导入 LLM 模块: {e}，标注功能将不可用")
+            self.model_client = None
+        except Exception as e:
+            logger.error(f"LLM 客户端初始化失败: {e}")
+            self.model_client = None
+
+    async def sample_messages(
+        self,
+        days: int = 7,
+        min_length: int = 5,
+        max_samples: int = 1000,
+        priority_ranges: list[tuple[float, float]] | None = None,
+    ) -> list[dict[str, Any]]:
+        """从数据库采样消息
+        
+        Args:
+            days: 采样最近 N 天的消息
+            min_length: 最小消息长度
+            max_samples: 最大采样数量
+            priority_ranges: 优先采样的兴趣分范围列表，如 [(0.4, 0.6)]
+            
+        Returns:
+            消息样本列表
+        """
+        from src.common.database.api.query import QueryBuilder
+        from src.common.database.core.models import Messages
+
+        logger.info(f"开始采样消息，时间范围: 最近 {days} 天")
+
+        # 查询条件
+        cutoff_time = datetime.now() - timedelta(days=days)
+        cutoff_ts = cutoff_time.timestamp()
+        query_builder = QueryBuilder(Messages)
+
+        # 获取所有符合条件的消息（使用 as_dict 方便访问字段）
+        messages = await query_builder.filter(
+            time__gte=cutoff_ts,
+        ).all(as_dict=True)
+
+        logger.info(f"查询到 {len(messages)} 条消息")
+
+        # 过滤消息长度
+        filtered = []
+        for msg in messages:
+            text = msg.get("processed_plain_text") or msg.get("display_message") or ""
+            if text and len(text.strip()) >= min_length:
+                filtered.append({**msg, "message_text": text})
+
+        logger.info(f"过滤后剩余 {len(filtered)} 条消息")
+
+        # 优先采样策略
+        if priority_ranges and len(filtered) > max_samples:
+            # 随机采样
+            samples = random.sample(filtered, max_samples)
+        else:
+            samples = filtered[:max_samples]
+
+        # 转换为字典格式
+        result = []
+        for msg in samples:
+            result.append({
+                "message_id": msg.get("message_id"),
+                "user_id": msg.get("user_id"),
+                "chat_id": msg.get("chat_id"),
+                "message_text": msg.get("message_text", ""),
+                "timestamp": msg.get("time"),
+                "platform": msg.get("chat_info_platform"),
+            })
+
+        logger.info(f"采样完成，共 {len(result)} 条消息")
+        return result
+
+    async def annotate_message(
+        self,
+        message_text: str,
+        persona_info: dict[str, Any],
+    ) -> int:
+        """使用 LLM 标注单条消息
+        
+        Args:
+            message_text: 消息文本
+            persona_info: 人格信息
+            
+        Returns:
+            标签 (-1, 0, 1)
+        """
+        if not self.model_client:
+            await self.initialize()
+
+        # 构造人格描述
+        persona_desc = self._format_persona_info(persona_info)
+
+        # 构造提示词
+        prompt = self.ANNOTATION_PROMPT.format(
+            persona_info=persona_desc,
+            message_text=message_text,
+        )
+
+        try:
+            if not self.model_client:
+                logger.warning("LLM 客户端未初始化，返回默认标签")
+                return 0
+
+            # 调用 LLM
+            response = await self.model_client.generate_response_async(
+                prompt=prompt,
+                max_tokens=10,
+                temperature=0.1,  # 低温度保证一致性
+            )
+
+            # 解析响应
+            label = self._parse_label(response)
+            return label
+
+        except Exception as e:
+            logger.error(f"LLM 标注失败: {e}")
+            return 0  # 默认返回中立
+
+    async def annotate_batch(
+        self,
+        messages: list[dict[str, Any]],
+        persona_info: dict[str, Any],
+        save_path: Path | None = None,
+        batch_size: int = 20,
+    ) -> list[dict[str, Any]]:
+        """批量标注消息（真正的批量模式）
+        
+        Args:
+            messages: 消息列表
+            persona_info: 人格信息
+            save_path: 保存路径（可选）
+            batch_size: 每次LLM请求处理的消息数（默认20）
+            
+        Returns:
+            标注后的数据集
+        """
+        logger.info(f"开始批量标注，共 {len(messages)} 条消息，每批 {batch_size} 条")
+
+        annotated_data = []
+
+        for i in range(0, len(messages), batch_size):
+            batch = messages[i : i + batch_size]
+            
+            # 批量标注（一次LLM请求处理多条消息）
+            labels = await self._annotate_batch_llm(batch, persona_info)
+            
+            # 保存结果
+            for msg, label in zip(batch, labels):
+                annotated_data.append({
+                    "message_id": msg["message_id"],
+                    "message_text": msg["message_text"],
+                    "label": label,
+                    "user_id": msg.get("user_id"),
+                    "chat_id": msg.get("chat_id"),
+                    "timestamp": msg.get("timestamp"),
+                })
+
+            logger.info(f"已标注 {len(annotated_data)}/{len(messages)} 条")
+
+        # 统计标签分布
+        label_counts = {}
+        for item in annotated_data:
+            label = item["label"]
+            label_counts[label] = label_counts.get(label, 0) + 1
+
+        logger.info(f"标注完成，标签分布: {label_counts}")
+
+        # 保存到文件
+        if save_path:
+            save_path.parent.mkdir(parents=True, exist_ok=True)
+            with open(save_path, "w", encoding="utf-8") as f:
+                json.dump(annotated_data, f, ensure_ascii=False, indent=2)
+            logger.info(f"数据集已保存到: {save_path}")
+
+        return annotated_data
+
+    async def _annotate_batch_llm(
+        self,
+        messages: list[dict[str, Any]],
+        persona_info: dict[str, Any],
+    ) -> list[int]:
+        """使用一次LLM请求标注多条消息
+        
+        Args:
+            messages: 消息列表（通常20条）
+            persona_info: 人格信息
+            
+        Returns:
+            标签列表
+        """
+        if not self.model_client:
+            logger.warning("LLM 客户端未初始化，返回默认标签")
+            return [0] * len(messages)
+
+        # 构造人格描述
+        persona_desc = self._format_persona_info(persona_info)
+
+        # 构造消息列表
+        messages_list = ""
+        for idx, msg in enumerate(messages, 1):
+            messages_list += f"{idx}. {msg['message_text']}\n"
+
+        # 构造示例输出
+        example_output = json.dumps(
+            {str(i): 0 for i in range(1, len(messages) + 1)},
+            ensure_ascii=False,
+            indent=2
+        )
+
+        # 构造提示词
+        prompt = self.BATCH_ANNOTATION_PROMPT.format(
+            persona_info=persona_desc,
+            messages_list=messages_list,
+            example_output=example_output,
+        )
+
+        try:
+            # 调用 LLM（使用更大的token限制）
+            response = await self.model_client.generate_response_async(
+                prompt=prompt,
+                max_tokens=500,  # 批量标注需要更多token
+                temperature=0.1,
+            )
+
+            # 解析批量响应
+            labels = self._parse_batch_labels(response, len(messages))
+            return labels
+
+        except Exception as e:
+            logger.error(f"批量LLM标注失败: {e}，返回默认值")
+            return [0] * len(messages)
+
+    def _format_persona_info(self, persona_info: dict[str, Any]) -> str:
+        """格式化人格信息
+        
+        Args:
+            persona_info: 人格信息字典
+            
+        Returns:
+            格式化后的人格描述
+        """
+        parts = []
+
+        if "name" in persona_info:
+            parts.append(f"角色名称: {persona_info['name']}")
+
+        if "interests" in persona_info:
+            parts.append(f"兴趣点: {', '.join(persona_info['interests'])}")
+
+        if "dislikes" in persona_info:
+            parts.append(f"厌恶点: {', '.join(persona_info['dislikes'])}")
+
+        if "personality" in persona_info:
+            parts.append(f"性格特点: {persona_info['personality']}")
+
+        return "\n".join(parts) if parts else "无特定人格设定"
+
+    def _parse_label(self, response: str) -> int:
+        """解析 LLM 响应为标签
+        
+        Args:
+            response: LLM 响应文本
+            
+        Returns:
+            标签 (-1, 0, 1)
+        """
+        # 部分 LLM 客户端可能返回 (text, meta) 的 tuple，这里取首元素并转为字符串
+        if isinstance(response, (tuple, list)):
+            response = response[0] if response else ""
+        response = str(response).strip()
+
+        # 尝试直接解析数字
+        if response in ["-1", "0", "1"]:
+            return int(response)
+
+        # 尝试提取数字
+        if "-1" in response:
+            return -1
+        elif "1" in response:
+            return 1
+        elif "0" in response:
+            return 0
+
+        # 默认返回中立
+        logger.warning(f"无法解析 LLM 响应: {response}，返回默认值 0")
+        return 0
+
+    def _parse_batch_labels(self, response: str, expected_count: int) -> list[int]:
+        """解析批量LLM响应为标签列表
+        
+        Args:
+            response: LLM 响应文本（JSON格式）
+            expected_count: 期望的标签数量
+            
+        Returns:
+            标签列表
+        """
+        try:
+            # 兼容 tuple/list 返回格式
+            if isinstance(response, (tuple, list)):
+                response = response[0] if response else ""
+            response = str(response)
+
+            # 提取JSON内容
+            import re
+            json_match = re.search(r'```json\s*({.*?})\s*```', response, re.DOTALL)
+            if json_match:
+                json_str = json_match.group(1)
+            else:
+                # 尝试直接解析
+                json_str = response
+            import json_repair
+            # 解析JSON
+            labels_json = json_repair.repair_json(json_str)
+            labels_dict = json.loads(labels_json)  # 验证是否为有效JSON
+            # 转换为列表
+            labels = []
+            for i in range(1, expected_count + 1):
+                key = str(i)
+                if key in labels_dict:
+                    label = labels_dict[key]
+                    # 确保标签值有效
+                    if label in [-1, 0, 1]:
+                        labels.append(label)
+                    else:
+                        logger.warning(f"无效标签值 {label}，使用默认值 0")
+                        labels.append(0)
+                else:
+                    # 尝试从值列表或数组中顺序取值
+                    if isinstance(labels_dict, list) and len(labels_dict) >= i:
+                        label = labels_dict[i - 1]
+                        labels.append(label if label in [-1, 0, 1] else 0)
+                    else:
+                        labels.append(0)
+
+            if len(labels) != expected_count:
+                logger.warning(
+                    f"标签数量不匹配：期望 {expected_count}，实际 {len(labels)}，"
+                    f"补齐为 {expected_count}"
+                )
+                # 补齐或截断
+                if len(labels) < expected_count:
+                    labels.extend([0] * (expected_count - len(labels)))
+                else:
+                    labels = labels[:expected_count]
+
+            return labels
+
+        except json.JSONDecodeError as e:
+            logger.error(f"JSON解析失败: {e}，响应内容: {response[:200]}")
+            return [0] * expected_count
+        except Exception as e:
+            # 兜底：尝试直接提取所有标签数字
+            try:
+                import re
+                numbers = re.findall(r"-?1|0", response)
+                labels = [int(n) for n in numbers[:expected_count]]
+                if len(labels) < expected_count:
+                    labels.extend([0] * (expected_count - len(labels)))
+                return labels
+            except Exception:
+                logger.error(f"批量标签解析失败: {e}")
+                return [0] * expected_count
+
+    @staticmethod
+    def load_dataset(path: Path) -> tuple[list[str], list[int]]:
+        """加载训练数据集
+        
+        Args:
+            path: 数据集文件路径
+            
+        Returns:
+            (文本列表, 标签列表)
+        """
+        with open(path, "r", encoding="utf-8") as f:
+            data = json.load(f)
+
+        texts = [item["message_text"] for item in data]
+        labels = [item["label"] for item in data]
+
+        logger.info(f"加载数据集: {len(texts)} 条样本")
+        return texts, labels
+
+
+async def generate_training_dataset(
+    output_path: Path,
+    persona_info: dict[str, Any],
+    days: int = 7,
+    max_samples: int = 1000,
+    model_name: str | None = None,
+) -> Path:
+    """生成训练数据集（主函数）
+    
+    Args:
+        output_path: 输出文件路径
+        persona_info: 人格信息
+        days: 采样最近 N 天的消息
+        max_samples: 最大采样数
+        model_name: LLM 模型名称
+        
+    Returns:
+        保存的文件路径
+    """
+    generator = DatasetGenerator(model_name=model_name)
+    await generator.initialize()
+
+    # 采样消息
+    messages = await generator.sample_messages(
+        days=days,
+        max_samples=max_samples,
+    )
+
+    # 批量标注
+    await generator.annotate_batch(
+        messages=messages,
+        persona_info=persona_info,
+        save_path=output_path,
+    )
+
+    return output_path

src/chat/semantic_interest/features_tfidf.py

@@ -0,0 +1,142 @@
+"""TF-IDF 特征向量化器
+
+使用字符级 n-gram 提取中文消息的 TF-IDF 特征
+"""
+
+from pathlib import Path
+
+from sklearn.feature_extraction.text import TfidfVectorizer
+
+from src.common.logger import get_logger
+
+logger = get_logger("semantic_interest.features")
+
+
+class TfidfFeatureExtractor:
+    """TF-IDF 特征提取器
+    
+    使用字符级 n-gram 策略，适合中文/多语言场景
+    """
+
+    def __init__(
+        self,
+        analyzer: str = "char",  # type: ignore
+        ngram_range: tuple[int, int] = (2, 4),
+        max_features: int = 20000,
+        min_df: int = 5,
+        max_df: float = 0.95,
+    ):
+        """初始化特征提取器
+        
+        Args:
+            analyzer: 分析器类型 ('char' 或 'word')
+            ngram_range: n-gram 范围，例如 (2, 4) 表示 2~4 字符的 n-gram
+            max_features: 词表最大大小，防止特征爆炸
+            min_df: 最小文档频率，至少出现在 N 个样本中才纳入词表
+            max_df: 最大文档频率，出现频率超过此比例的词将被过滤（如停用词）
+        """
+        self.vectorizer = TfidfVectorizer(
+            analyzer=analyzer,
+            ngram_range=ngram_range,
+            max_features=max_features,
+            min_df=min_df,
+            max_df=max_df,
+            lowercase=True,
+            strip_accents=None,  # 保留中文字符
+            sublinear_tf=True,  # 使用对数 TF 缩放
+            norm="l2",  # L2 归一化
+        )
+        self.is_fitted = False
+
+        logger.info(
+            f"TF-IDF 特征提取器初始化: analyzer={analyzer}, "
+            f"ngram_range={ngram_range}, max_features={max_features}"
+        )
+
+    def fit(self, texts: list[str]) -> "TfidfFeatureExtractor":
+        """训练向量化器
+        
+        Args:
+            texts: 训练文本列表
+            
+        Returns:
+            self
+        """
+        logger.info(f"开始训练 TF-IDF 向量化器，样本数: {len(texts)}")
+        self.vectorizer.fit(texts)
+        self.is_fitted = True
+        
+        vocab_size = len(self.vectorizer.vocabulary_)
+        logger.info(f"TF-IDF 向量化器训练完成，词表大小: {vocab_size}")
+        
+        return self
+
+    def transform(self, texts: list[str]):
+        """将文本转换为 TF-IDF 向量
+        
+        Args:
+            texts: 待转换文本列表
+            
+        Returns:
+            稀疏矩阵
+        """
+        if not self.is_fitted:
+            raise ValueError("向量化器尚未训练，请先调用 fit() 方法")
+            
+        return self.vectorizer.transform(texts)
+
+    def fit_transform(self, texts: list[str]):
+        """训练并转换文本
+        
+        Args:
+            texts: 训练文本列表
+            
+        Returns:
+            稀疏矩阵
+        """
+        logger.info(f"开始训练并转换 TF-IDF 向量，样本数: {len(texts)}")
+        result = self.vectorizer.fit_transform(texts)
+        self.is_fitted = True
+        
+        vocab_size = len(self.vectorizer.vocabulary_)
+        logger.info(f"TF-IDF 向量化完成，词表大小: {vocab_size}")
+        
+        return result
+
+    def get_feature_names(self) -> list[str]:
+        """获取特征名称列表
+        
+        Returns:
+            特征名称列表
+        """
+        if not self.is_fitted:
+            raise ValueError("向量化器尚未训练")
+            
+        return self.vectorizer.get_feature_names_out().tolist()
+
+    def get_vocabulary_size(self) -> int:
+        """获取词表大小
+        
+        Returns:
+            词表大小
+        """
+        if not self.is_fitted:
+            return 0
+        return len(self.vectorizer.vocabulary_)
+
+    def get_config(self) -> dict:
+        """获取配置信息
+        
+        Returns:
+            配置字典
+        """
+        params = self.vectorizer.get_params()
+        return {
+            "analyzer": params["analyzer"],
+            "ngram_range": params["ngram_range"],
+            "max_features": params["max_features"],
+            "min_df": params["min_df"],
+            "max_df": params["max_df"],
+            "vocabulary_size": self.get_vocabulary_size() if self.is_fitted else 0,
+            "is_fitted": self.is_fitted,
+        }

src/chat/semantic_interest/model_lr.py

@@ -0,0 +1,265 @@
+"""Logistic Regression 模型训练与推理
+
+使用多分类 Logistic Regression 预测消息的兴趣度标签 (-1, 0, 1)
+"""
+
+import time
+from pathlib import Path
+from typing import Any
+
+import joblib
+import numpy as np
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import classification_report, confusion_matrix
+from sklearn.model_selection import train_test_split
+
+from src.common.logger import get_logger
+from src.chat.semantic_interest.features_tfidf import TfidfFeatureExtractor
+
+logger = get_logger("semantic_interest.model")
+
+
+class SemanticInterestModel:
+    """语义兴趣度模型
+    
+    使用 Logistic Regression 进行多分类（-1: 不感兴趣, 0: 中立, 1: 感兴趣）
+    """
+
+    def __init__(
+        self,
+        class_weight: str | dict | None = "balanced",
+        max_iter: int = 1000,
+        solver: str = "lbfgs",  # type: ignore
+        n_jobs: int = -1,
+    ):
+        """初始化模型
+        
+        Args:
+            class_weight: 类别权重配置
+                - "balanced": 自动平衡类别权重
+                - dict: 自定义权重，如 {-1: 0.8, 0: 0.6, 1: 1.6}
+                - None: 不使用权重
+            max_iter: 最大迭代次数
+            solver: 求解器 ('lbfgs', 'saga', 'liblinear' 等)
+            n_jobs: 并行任务数，-1 表示使用所有 CPU 核心
+        """
+        self.clf = LogisticRegression(
+            multi_class="multinomial",
+            solver=solver,
+            max_iter=max_iter,
+            class_weight=class_weight,
+            n_jobs=n_jobs,
+            random_state=42,
+        )
+        self.is_fitted = False
+        self.label_mapping = {-1: 0, 0: 1, 1: 2}  # 内部类别映射
+        self.training_metrics = {}
+
+        logger.info(
+            f"Logistic Regression 模型初始化: class_weight={class_weight}, "
+            f"max_iter={max_iter}, solver={solver}"
+        )
+
+    def train(
+        self,
+        X_train,
+        y_train,
+        X_val=None,
+        y_val=None,
+        verbose: bool = True,
+    ) -> dict[str, Any]:
+        """训练模型
+        
+        Args:
+            X_train: 训练集特征矩阵
+            y_train: 训练集标签（-1, 0, 1）
+            X_val: 验证集特征矩阵（可选）
+            y_val: 验证集标签（可选）
+            verbose: 是否输出详细日志
+            
+        Returns:
+            训练指标字典
+        """
+        start_time = time.time()
+        logger.info(f"开始训练模型，训练样本数: {len(y_train)}")
+
+        # 训练模型
+        self.clf.fit(X_train, y_train)
+        self.is_fitted = True
+
+        training_time = time.time() - start_time
+        logger.info(f"模型训练完成，耗时: {training_time:.2f}秒")
+
+        # 计算训练集指标
+        y_train_pred = self.clf.predict(X_train)
+        train_accuracy = (y_train_pred == y_train).mean()
+
+        metrics = {
+            "training_time": training_time,
+            "train_accuracy": train_accuracy,
+            "train_samples": len(y_train),
+        }
+
+        if verbose:
+            logger.info(f"训练集准确率: {train_accuracy:.4f}")
+            logger.info(f"类别分布: {dict(zip(*np.unique(y_train, return_counts=True)))}")
+
+        # 如果提供了验证集，计算验证指标
+        if X_val is not None and y_val is not None:
+            val_metrics = self.evaluate(X_val, y_val, verbose=verbose)
+            metrics.update(val_metrics)
+
+        self.training_metrics = metrics
+        return metrics
+
+    def evaluate(
+        self,
+        X_test,
+        y_test,
+        verbose: bool = True,
+    ) -> dict[str, Any]:
+        """评估模型
+        
+        Args:
+            X_test: 测试集特征矩阵
+            y_test: 测试集标签
+            verbose: 是否输出详细日志
+            
+        Returns:
+            评估指标字典
+        """
+        if not self.is_fitted:
+            raise ValueError("模型尚未训练")
+
+        y_pred = self.clf.predict(X_test)
+        accuracy = (y_pred == y_test).mean()
+
+        metrics = {
+            "test_accuracy": accuracy,
+            "test_samples": len(y_test),
+        }
+
+        if verbose:
+            logger.info(f"测试集准确率: {accuracy:.4f}")
+            logger.info("\n分类报告:")
+            report = classification_report(
+                y_test,
+                y_pred,
+                labels=[-1, 0, 1],
+                target_names=["不感兴趣(-1)", "中立(0)", "感兴趣(1)"],
+                zero_division=0,
+            )
+            logger.info(f"\n{report}")
+
+            logger.info("\n混淆矩阵:")
+            cm = confusion_matrix(y_test, y_pred, labels=[-1, 0, 1])
+            logger.info(f"\n{cm}")
+
+        return metrics
+
+    def predict_proba(self, X) -> np.ndarray:
+        """预测概率分布
+        
+        Args:
+            X: 特征矩阵
+            
+        Returns:
+            概率矩阵，形状为 (n_samples, 3)，对应 [-1, 0, 1] 的概率
+        """
+        if not self.is_fitted:
+            raise ValueError("模型尚未训练")
+
+        proba = self.clf.predict_proba(X)
+
+        # 确保类别顺序为 [-1, 0, 1]
+        classes = self.clf.classes_
+        if not np.array_equal(classes, [-1, 0, 1]):
+            # 需要重新排序
+            sorted_proba = np.zeros_like(proba)
+            for i, cls in enumerate([-1, 0, 1]):
+                idx = np.where(classes == cls)[0]
+                if len(idx) > 0:
+                    sorted_proba[:, i] = proba[:, idx[0]]
+            return sorted_proba
+
+        return proba
+
+    def predict(self, X) -> np.ndarray:
+        """预测类别
+        
+        Args:
+            X: 特征矩阵
+            
+        Returns:
+            预测标签数组
+        """
+        if not self.is_fitted:
+            raise ValueError("模型尚未训练")
+
+        return self.clf.predict(X)
+
+    def get_config(self) -> dict:
+        """获取模型配置
+        
+        Returns:
+            配置字典
+        """
+        params = self.clf.get_params()
+        return {
+            "multi_class": params["multi_class"],
+            "solver": params["solver"],
+            "max_iter": params["max_iter"],
+            "class_weight": params["class_weight"],
+            "is_fitted": self.is_fitted,
+            "classes": self.clf.classes_.tolist() if self.is_fitted else None,
+        }
+
+
+def train_semantic_model(
+    texts: list[str],
+    labels: list[int],
+    test_size: float = 0.1,
+    random_state: int = 42,
+    tfidf_config: dict | None = None,
+    model_config: dict | None = None,
+) -> tuple[TfidfFeatureExtractor, SemanticInterestModel, dict]:
+    """训练完整的语义兴趣度模型
+    
+    Args:
+        texts: 消息文本列表
+        labels: 对应的标签列表 (-1, 0, 1)
+        test_size: 验证集比例
+        random_state: 随机种子
+        tfidf_config: TF-IDF 配置
+        model_config: 模型配置
+        
+    Returns:
+        (特征提取器, 模型, 训练指标)
+    """
+    logger.info(f"开始训练语义兴趣度模型，总样本数: {len(texts)}")
+
+    # 划分训练集和验证集
+    X_train_texts, X_val_texts, y_train, y_val = train_test_split(
+        texts,
+        labels,
+        test_size=test_size,
+        stratify=labels,
+        random_state=random_state,
+    )
+
+    logger.info(f"训练集: {len(X_train_texts)}, 验证集: {len(X_val_texts)}")
+
+    # 初始化并训练 TF-IDF 向量化器
+    tfidf_config = tfidf_config or {}
+    feature_extractor = TfidfFeatureExtractor(**tfidf_config)
+    X_train = feature_extractor.fit_transform(X_train_texts)
+    X_val = feature_extractor.transform(X_val_texts)
+
+    # 初始化并训练模型
+    model_config = model_config or {}
+    model = SemanticInterestModel(**model_config)
+    metrics = model.train(X_train, y_train, X_val, y_val)
+
+    logger.info("语义兴趣度模型训练完成")
+
+    return feature_extractor, model, metrics

src/chat/semantic_interest/runtime_scorer.py

@@ -0,0 +1,408 @@
+"""运行时语义兴趣度评分器
+
+在线推理时使用，提供快速的兴趣度评分
+"""
+
+import asyncio
+import time
+from pathlib import Path
+from typing import Any
+
+import joblib
+import numpy as np
+
+from src.common.logger import get_logger
+from src.chat.semantic_interest.features_tfidf import TfidfFeatureExtractor
+from src.chat.semantic_interest.model_lr import SemanticInterestModel
+
+logger = get_logger("semantic_interest.scorer")
+
+
+class SemanticInterestScorer:
+    """语义兴趣度评分器
+    
+    加载训练好的模型，在运行时快速计算消息的语义兴趣度
+    """
+
+    def __init__(self, model_path: str | Path):
+        """初始化评分器
+        
+        Args:
+            model_path: 模型文件路径 (.pkl)
+        """
+        self.model_path = Path(model_path)
+        self.vectorizer: TfidfFeatureExtractor | None = None
+        self.model: SemanticInterestModel | None = None
+        self.meta: dict[str, Any] = {}
+        self.is_loaded = False
+
+        # 统计信息
+        self.total_scores = 0
+        self.total_time = 0.0
+
+    def load(self):
+        """加载模型"""
+        if not self.model_path.exists():
+            raise FileNotFoundError(f"模型文件不存在: {self.model_path}")
+
+        logger.info(f"开始加载模型: {self.model_path}")
+        start_time = time.time()
+
+        try:
+            bundle = joblib.load(self.model_path)
+
+            self.vectorizer = bundle["vectorizer"]
+            self.model = bundle["model"]
+            self.meta = bundle.get("meta", {})
+
+            self.is_loaded = True
+            load_time = time.time() - start_time
+
+            logger.info(
+                f"模型加载成功，耗时: {load_time:.3f}秒, "
+                f"词表大小: {self.vectorizer.get_vocabulary_size()}"  # type: ignore
+            )
+
+            if self.meta:
+                logger.info(f"模型元信息: {self.meta}")
+
+        except Exception as e:
+            logger.error(f"模型加载失败: {e}")
+            raise
+
+    def reload(self):
+        """重新加载模型（热更新）"""
+        logger.info("重新加载模型...")
+        self.is_loaded = False
+        self.load()
+
+    def score(self, text: str) -> float:
+        """计算单条消息的语义兴趣度
+        
+        Args:
+            text: 消息文本
+            
+        Returns:
+            兴趣分 [0.0, 1.0]，越高表示越感兴趣
+        """
+        if not self.is_loaded:
+            raise ValueError("模型尚未加载，请先调用 load() 方法")
+
+        start_time = time.time()
+
+        try:
+            # 向量化
+            X = self.vectorizer.transform([text])
+
+            # 预测概率
+            proba = self.model.predict_proba(X)[0]
+
+            # proba 顺序为 [-1, 0, 1]
+            p_neg, p_neu, p_pos = proba
+
+            # 兴趣分计算策略：
+            # interest = P(1) + 0.5 * P(0)
+            # 这样：纯正向(1)=1.0, 纯中立(0)=0.5, 纯负向(-1)=0.0
+            interest = float(p_pos + 0.5 * p_neu)
+
+            # 确保在 [0, 1] 范围内
+            interest = max(0.0, min(1.0, interest))
+
+            # 统计
+            self.total_scores += 1
+            self.total_time += time.time() - start_time
+
+            return interest
+
+        except Exception as e:
+            logger.error(f"兴趣度计算失败: {e}, 消息: {text[:50]}")
+            return 0.5  # 默认返回中立值
+
+    async def score_async(self, text: str) -> float:
+        """异步计算兴趣度
+        
+        Args:
+            text: 消息文本
+            
+        Returns:
+            兴趣分 [0.0, 1.0]
+        """
+        # 在线程池中执行，避免阻塞事件循环
+        loop = asyncio.get_event_loop()
+        return await loop.run_in_executor(None, self.score, text)
+
+    def score_batch(self, texts: list[str]) -> list[float]:
+        """批量计算兴趣度
+        
+        Args:
+            texts: 消息文本列表
+            
+        Returns:
+            兴趣分列表
+        """
+        if not self.is_loaded:
+            raise ValueError("模型尚未加载")
+
+        if not texts:
+            return []
+
+        start_time = time.time()
+
+        try:
+            # 批量向量化
+            X = self.vectorizer.transform(texts)
+
+            # 批量预测
+            proba = self.model.predict_proba(X)
+
+            # 计算兴趣分
+            interests = []
+            for p_neg, p_neu, p_pos in proba:
+                interest = float(p_pos + 0.5 * p_neu)
+                interest = max(0.0, min(1.0, interest))
+                interests.append(interest)
+
+            # 统计
+            self.total_scores += len(texts)
+            self.total_time += time.time() - start_time
+
+            return interests
+
+        except Exception as e:
+            logger.error(f"批量兴趣度计算失败: {e}")
+            return [0.5] * len(texts)
+
+    def get_detailed_score(self, text: str) -> dict[str, Any]:
+        """获取详细的兴趣度评分信息
+        
+        Args:
+            text: 消息文本
+            
+        Returns:
+            包含概率分布和最终分数的详细信息
+        """
+        if not self.is_loaded:
+            raise ValueError("模型尚未加载")
+
+        X = self.vectorizer.transform([text])
+        proba = self.model.predict_proba(X)[0]
+        pred_label = self.model.predict(X)[0]
+
+        p_neg, p_neu, p_pos = proba
+        interest = float(p_pos + 0.5 * p_neu)
+
+        return {
+            "interest_score": max(0.0, min(1.0, interest)),
+            "proba_distribution": {
+                "dislike": float(p_neg),
+                "neutral": float(p_neu),
+                "like": float(p_pos),
+            },
+            "predicted_label": int(pred_label),
+            "text_preview": text[:100],
+        }
+
+    def get_statistics(self) -> dict[str, Any]:
+        """获取评分器统计信息
+        
+        Returns:
+            统计信息字典
+        """
+        avg_time = self.total_time / self.total_scores if self.total_scores > 0 else 0
+
+        return {
+            "is_loaded": self.is_loaded,
+            "model_path": str(self.model_path),
+            "total_scores": self.total_scores,
+            "total_time": self.total_time,
+            "avg_score_time": avg_time,
+            "vocabulary_size": (
+                self.vectorizer.get_vocabulary_size()
+                if self.vectorizer and self.is_loaded
+                else 0
+            ),
+            "meta": self.meta,
+        }
+
+    def __repr__(self) -> str:
+        return (
+            f"SemanticInterestScorer("
+            f"loaded={self.is_loaded}, "
+            f"model={self.model_path.name})"
+        )
+
+
+class ModelManager:
+    """模型管理器
+    
+    支持模型热更新、版本管理和人设感知的模型切换
+    """
+
+    def __init__(self, model_dir: Path):
+        """初始化管理器
+        
+        Args:
+            model_dir: 模型目录
+        """
+        self.model_dir = Path(model_dir)
+        self.model_dir.mkdir(parents=True, exist_ok=True)
+
+        self.current_scorer: SemanticInterestScorer | None = None
+        self.current_version: str | None = None
+        self.current_persona_info: dict[str, Any] | None = None
+        self._lock = asyncio.Lock()
+        
+        # 自动训练器集成
+        self._auto_trainer = None
+
+    async def load_model(self, version: str = "latest", persona_info: dict[str, Any] | None = None) -> SemanticInterestScorer:
+        """加载指定版本的模型，支持人设感知
+        
+        Args:
+            version: 模型版本号或 "latest" 或 "auto"
+            persona_info: 人设信息，用于自动选择匹配的模型
+            
+        Returns:
+            评分器实例
+        """
+        async with self._lock:
+            # 如果指定了人设信息，尝试使用自动训练器
+            if persona_info is not None and version == "auto":
+                model_path = await self._get_persona_model(persona_info)
+            elif version == "latest":
+                model_path = self._get_latest_model()
+            else:
+                model_path = self.model_dir / f"semantic_interest_{version}.pkl"
+
+            if not model_path or not model_path.exists():
+                raise FileNotFoundError(f"模型文件不存在: {model_path}")
+
+            scorer = SemanticInterestScorer(model_path)
+            scorer.load()
+
+            self.current_scorer = scorer
+            self.current_version = version
+            self.current_persona_info = persona_info
+
+            logger.info(f"模型管理器已加载版本: {version}, 文件: {model_path.name}")
+            return scorer
+
+    async def reload_current_model(self):
+        """重新加载当前模型"""
+        if not self.current_scorer:
+            raise ValueError("尚未加载任何模型")
+
+        async with self._lock:
+            self.current_scorer.reload()
+            logger.info("模型已重新加载")
+
+    def _get_latest_model(self) -> Path:
+        """获取最新的模型文件
+        
+        Returns:
+            最新模型文件路径
+        """
+        model_files = list(self.model_dir.glob("semantic_interest_*.pkl"))
+
+        if not model_files:
+            raise FileNotFoundError(f"在 {self.model_dir} 中未找到模型文件")
+
+        # 按修改时间排序
+        latest = max(model_files, key=lambda p: p.stat().st_mtime)
+        return latest
+
+    def get_scorer(self) -> SemanticInterestScorer:
+        """获取当前评分器
+        
+        Returns:
+            当前评分器实例
+        """
+        if not self.current_scorer:
+            raise ValueError("尚未加载任何模型")
+
+        return self.current_scorer
+
+    async def _get_persona_model(self, persona_info: dict[str, Any]) -> Path | None:
+        """根据人设信息获取或训练模型
+        
+        Args:
+            persona_info: 人设信息
+            
+        Returns:
+            模型文件路径
+        """
+        try:
+            # 延迟导入避免循环依赖
+            from src.chat.semantic_interest.auto_trainer import get_auto_trainer
+            
+            if self._auto_trainer is None:
+                self._auto_trainer = get_auto_trainer()
+            
+            # 检查是否需要训练
+            trained, model_path = await self._auto_trainer.auto_train_if_needed(
+                persona_info=persona_info,
+                days=7,
+                max_samples=500,
+            )
+            
+            if trained and model_path:
+                logger.info(f"[模型管理器] 使用新训练的模型: {model_path.name}")
+                return model_path
+            
+            # 获取现有的人设模型
+            model_path = self._auto_trainer.get_model_for_persona(persona_info)
+            if model_path:
+                return model_path
+            
+            # 降级到 latest
+            logger.warning("[模型管理器] 未找到人设模型，使用 latest")
+            return self._get_latest_model()
+            
+        except Exception as e:
+            logger.error(f"[模型管理器] 获取人设模型失败: {e}")
+            return self._get_latest_model()
+
+    async def check_and_reload_for_persona(self, persona_info: dict[str, Any]) -> bool:
+        """检查人设变化并重新加载模型
+        
+        Args:
+            persona_info: 当前人设信息
+            
+        Returns:
+            True 如果重新加载了模型
+        """
+        # 检查人设是否变化
+        if self.current_persona_info == persona_info:
+            return False
+        
+        logger.info("[模型管理器] 检测到人设变化，重新加载模型...")
+        
+        try:
+            await self.load_model(version="auto", persona_info=persona_info)
+            return True
+        except Exception as e:
+            logger.error(f"[模型管理器] 重新加载模型失败: {e}")
+            return False
+
+    async def start_auto_training(self, persona_info: dict[str, Any], interval_hours: int = 24):
+        """启动自动训练任务
+        
+        Args:
+            persona_info: 人设信息
+            interval_hours: 检查间隔（小时）
+        """
+        try:
+            from src.chat.semantic_interest.auto_trainer import get_auto_trainer
+            
+            if self._auto_trainer is None:
+                self._auto_trainer = get_auto_trainer()
+            
+            logger.info(f"[模型管理器] 启动自动训练任务，间隔: {interval_hours}小时")
+            
+            # 在后台任务中运行
+            asyncio.create_task(
+                self._auto_trainer.scheduled_train(persona_info, interval_hours)
+            )
+            
+        except Exception as e:
+            logger.error(f"[模型管理器] 启动自动训练失败: {e}")

src/chat/semantic_interest/trainer.py

@@ -0,0 +1,234 @@
+"""训练器入口脚本
+
+统一的训练流程入口，包含数据采样、标注、训练、评估
+"""
+
+import asyncio
+from datetime import datetime
+from pathlib import Path
+from typing import Any
+
+import joblib
+
+from src.common.logger import get_logger
+from src.chat.semantic_interest.dataset import DatasetGenerator, generate_training_dataset
+from src.chat.semantic_interest.model_lr import train_semantic_model
+
+logger = get_logger("semantic_interest.trainer")
+
+
+class SemanticInterestTrainer:
+    """语义兴趣度训练器
+    
+    统一管理训练流程
+    """
+
+    def __init__(
+        self,
+        data_dir: Path | None = None,
+        model_dir: Path | None = None,
+    ):
+        """初始化训练器
+        
+        Args:
+            data_dir: 数据集目录
+            model_dir: 模型保存目录
+        """
+        self.data_dir = Path(data_dir or "data/semantic_interest/datasets")
+        self.model_dir = Path(model_dir or "data/semantic_interest/models")
+
+        self.data_dir.mkdir(parents=True, exist_ok=True)
+        self.model_dir.mkdir(parents=True, exist_ok=True)
+
+    async def prepare_dataset(
+        self,
+        persona_info: dict[str, Any],
+        days: int = 7,
+        max_samples: int = 1000,
+        model_name: str | None = None,
+        dataset_name: str | None = None,
+    ) -> Path:
+        """准备训练数据集
+        
+        Args:
+            persona_info: 人格信息
+            days: 采样最近 N 天的消息
+            max_samples: 最大采样数
+            model_name: LLM 模型名称
+            dataset_name: 数据集名称（默认使用时间戳）
+            
+        Returns:
+            数据集文件路径
+        """
+        if dataset_name is None:
+            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+            dataset_name = f"dataset_{timestamp}"
+
+        output_path = self.data_dir / f"{dataset_name}.json"
+
+        logger.info(f"开始准备数据集: {dataset_name}")
+
+        await generate_training_dataset(
+            output_path=output_path,
+            persona_info=persona_info,
+            days=days,
+            max_samples=max_samples,
+            model_name=model_name,
+        )
+
+        return output_path
+
+    def train_model(
+        self,
+        dataset_path: Path,
+        model_version: str | None = None,
+        tfidf_config: dict | None = None,
+        model_config: dict | None = None,
+        test_size: float = 0.1,
+    ) -> tuple[Path, dict]:
+        """训练模型
+        
+        Args:
+            dataset_path: 数据集文件路径
+            model_version: 模型版本号（默认使用时间戳）
+            tfidf_config: TF-IDF 配置
+            model_config: 模型配置
+            test_size: 验证集比例
+            
+        Returns:
+            (模型文件路径, 训练指标)
+        """
+        logger.info(f"开始训练模型，数据集: {dataset_path}")
+
+        # 加载数据集
+        from src.chat.semantic_interest.dataset import DatasetGenerator
+        texts, labels = DatasetGenerator.load_dataset(dataset_path)
+
+        # 训练模型
+        vectorizer, model, metrics = train_semantic_model(
+            texts=texts,
+            labels=labels,
+            test_size=test_size,
+            tfidf_config=tfidf_config,
+            model_config=model_config,
+        )
+
+        # 保存模型
+        if model_version is None:
+            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+            model_version = timestamp
+
+        model_path = self.model_dir / f"semantic_interest_{model_version}.pkl"
+
+        bundle = {
+            "vectorizer": vectorizer,
+            "model": model,
+            "meta": {
+                "version": model_version,
+                "trained_at": datetime.now().isoformat(),
+                "dataset": str(dataset_path),
+                "train_samples": len(texts),
+                "metrics": metrics,
+                "tfidf_config": vectorizer.get_config(),
+                "model_config": model.get_config(),
+            },
+        }
+
+        joblib.dump(bundle, model_path)
+        logger.info(f"模型已保存到: {model_path}")
+
+        return model_path, metrics
+
+    async def full_training_pipeline(
+        self,
+        persona_info: dict[str, Any],
+        days: int = 7,
+        max_samples: int = 1000,
+        llm_model_name: str | None = None,
+        tfidf_config: dict | None = None,
+        model_config: dict | None = None,
+        dataset_name: str | None = None,
+        model_version: str | None = None,
+    ) -> tuple[Path, Path, dict]:
+        """完整训练流程
+        
+        Args:
+            persona_info: 人格信息
+            days: 采样天数
+            max_samples: 最大采样数
+            llm_model_name: LLM 模型名称
+            tfidf_config: TF-IDF 配置
+            model_config: 模型配置
+            dataset_name: 数据集名称
+            model_version: 模型版本
+            
+        Returns:
+            (数据集路径, 模型路径, 训练指标)
+        """
+        logger.info("开始完整训练流程")
+
+        # 1. 准备数据集
+        dataset_path = await self.prepare_dataset(
+            persona_info=persona_info,
+            days=days,
+            max_samples=max_samples,
+            model_name=llm_model_name,
+            dataset_name=dataset_name,
+        )
+
+        # 2. 训练模型
+        model_path, metrics = self.train_model(
+            dataset_path=dataset_path,
+            model_version=model_version,
+            tfidf_config=tfidf_config,
+            model_config=model_config,
+        )
+
+        logger.info("完整训练流程完成")
+        logger.info(f"数据集: {dataset_path}")
+        logger.info(f"模型: {model_path}")
+        logger.info(f"指标: {metrics}")
+
+        return dataset_path, model_path, metrics
+
+
+async def main():
+    """示例：训练一个语义兴趣度模型"""
+
+    # 示例人格信息
+    persona_info = {
+        "name": "小狐",
+        "interests": ["动漫", "游戏", "编程", "技术", "二次元"],
+        "dislikes": ["广告", "政治", "无聊闲聊"],
+        "personality": "活泼开朗，对新鲜事物充满好奇",
+    }
+
+    # 创建训练器
+    trainer = SemanticInterestTrainer()
+
+    # 执行完整训练流程
+    dataset_path, model_path, metrics = await trainer.full_training_pipeline(
+        persona_info=persona_info,
+        days=7,  # 使用最近 7 天的消息
+        max_samples=500,  # 采样 500 条消息
+        llm_model_name=None,  # 使用默认 LLM
+        tfidf_config={
+            "analyzer": "char",
+            "ngram_range": (2, 4),
+            "max_features": 15000,
+            "min_df": 3,
+        },
+        model_config={
+            "class_weight": "balanced",
+            "max_iter": 1000,
+        },
+    )
+
+    print(f"\n训练完成！")
+    print(f"数据集: {dataset_path}")
+    print(f"模型: {model_path}")
+    print(f"准确率: {metrics.get('test_accuracy', 0):.4f}")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

src/plugins/built_in/affinity_flow_chatter/core/affinity_interest_calculator.py

@@ -1,15 +1,16 @@
 """AffinityFlow 风格兴趣值计算组件
 
 基于原有的 AffinityFlow 兴趣度评分系统，提供标准化的兴趣值计算功能
+集成了语义兴趣度计算（TF-IDF + Logistic Regression）
 """
 
 import asyncio
 import time
-from typing import TYPE_CHECKING
+from pathlib import Path
+from typing import TYPE_CHECKING, Any
 
 import orjson
 
-from src.chat.interest_system import bot_interest_manager
 from src.common.logger import get_logger
 from src.config.config import global_config
 from src.plugin_system.base.base_interest_calculator import BaseInterestCalculator, InterestCalculationResult
@@ -36,18 +37,19 @@ class AffinityInterestCalculator(BaseInterestCalculator):
         # 从配置加载评分权重
         affinity_config = global_config.affinity_flow
         self.score_weights = {
-            "interest_match": affinity_config.keyword_match_weight,  # 兴趣匹配度权重
+            "semantic": 0.5,  # 语义兴趣度权重（核心维度）
             "relationship": affinity_config.relationship_weight,  # 关系分权重
             "mentioned": affinity_config.mention_bot_weight,  # 是否提及bot权重
         }
 
+        # 语义兴趣度评分器（替代原有的 embedding 兴趣匹配）
+        self.semantic_scorer = None
+        self.use_semantic_scoring = True  # 必须启用
+
         # 评分阈值
         self.reply_threshold = affinity_config.reply_action_interest_threshold  # 回复动作兴趣阈值
         self.mention_threshold = affinity_config.mention_bot_adjustment_threshold  # 提及bot后的调整阈值
 
-        # 兴趣匹配系统配置
-        self.use_smart_matching = True
-
         # 连续不回复概率提升
         self.no_reply_count = 0
         self.max_no_reply_count = affinity_config.max_no_reply_count
@@ -69,14 +71,17 @@ class AffinityInterestCalculator(BaseInterestCalculator):
         self.post_reply_boost_max_count = affinity_config.post_reply_boost_max_count
         self.post_reply_boost_decay_rate = affinity_config.post_reply_boost_decay_rate
 
-        logger.info("[Affinity兴趣计算器] 初始化完成:")
+        logger.info("[Affinity兴趣计算器] 初始化完成（基于语义兴趣度 TF-IDF+LR）:")
         logger.info(f"  - 权重配置: {self.score_weights}")
         logger.info(f"  - 回复阈值: {self.reply_threshold}")
-        logger.info(f"  - 智能匹配: {self.use_smart_matching}")
+        logger.info(f"  - 语义评分: {self.use_semantic_scoring} (TF-IDF + Logistic Regression)")
         logger.info(f"  - 回复后连续对话: {self.enable_post_reply_boost}")
         logger.info(f"  - 回复冷却减少: {self.reply_cooldown_reduction}")
         logger.info(f"  - 最大不回复计数: {self.max_no_reply_count}")
 
+        # 异步初始化语义评分器
+        asyncio.create_task(self._initialize_semantic_scorer())
+
     async def execute(self, message: "DatabaseMessages") -> InterestCalculationResult:
         """执行AffinityFlow风格的兴趣值计算"""
         try:
@@ -93,10 +98,9 @@ class AffinityInterestCalculator(BaseInterestCalculator):
             logger.debug(f"[Affinity兴趣计算] 消息内容: {content[:50]}...")
             logger.debug(f"[Affinity兴趣计算] 用户ID: {user_id}")
 
-            # 1. 计算兴趣匹配分
-            keywords = self._extract_keywords_from_database(message)
-            interest_match_score = await self._calculate_interest_match_score(message, content, keywords)
-            logger.debug(f"[Affinity兴趣计算] 兴趣匹配分: {interest_match_score}")
+            # 1. 计算语义兴趣度（核心维度，替代原 embedding 兴趣匹配）
+            semantic_score = await self._calculate_semantic_score(content)
+            logger.debug(f"[Affinity兴趣计算] 语义兴趣度（TF-IDF+LR）: {semantic_score}")
 
             # 2. 计算关系分
             relationship_score = await self._calculate_relationship_score(user_id)
@@ -108,12 +112,12 @@ class AffinityInterestCalculator(BaseInterestCalculator):
 
             # 4. 综合评分
             # 确保所有分数都是有效的 float 值
-            interest_match_score = float(interest_match_score) if interest_match_score is not None else 0.0
+            semantic_score = float(semantic_score) if semantic_score is not None else 0.0
             relationship_score = float(relationship_score) if relationship_score is not None else 0.0
             mentioned_score = float(mentioned_score) if mentioned_score is not None else 0.0
 
             raw_total_score = (
-                interest_match_score * self.score_weights["interest_match"]
+                semantic_score * self.score_weights["semantic"]
                 + relationship_score * self.score_weights["relationship"]
                 + mentioned_score * self.score_weights["mentioned"]
             )
@@ -122,7 +126,8 @@ class AffinityInterestCalculator(BaseInterestCalculator):
             total_score = min(raw_total_score, 1.0)
 
             logger.debug(
-                f"[Affinity兴趣计算] 综合得分计算: {interest_match_score:.3f}*{self.score_weights['interest_match']} + "
+                f"[Affinity兴趣计算] 综合得分计算: "
+                f"{semantic_score:.3f}*{self.score_weights['semantic']} + "
                 f"{relationship_score:.3f}*{self.score_weights['relationship']} + "
                 f"{mentioned_score:.3f}*{self.score_weights['mentioned']} = {raw_total_score:.3f}"
             )
@@ -153,7 +158,7 @@ class AffinityInterestCalculator(BaseInterestCalculator):
 
             logger.debug(
                 f"Affinity兴趣值计算完成 - 消息 {message_id}: {adjusted_score:.3f} "
-                f"(匹配:{interest_match_score:.2f}, 关系:{relationship_score:.2f}, 提及:{mentioned_score:.2f})"
+                f"(语义:{semantic_score:.2f}, 关系:{relationship_score:.2f}, 提及:{mentioned_score:.2f})"
             )
 
             return InterestCalculationResult(
@@ -172,55 +177,6 @@ class AffinityInterestCalculator(BaseInterestCalculator):
                 success=False, message_id=getattr(message, "message_id", ""), interest_value=0.0, error_message=str(e)
             )
 
-    async def _calculate_interest_match_score(
-        self, message: "DatabaseMessages", content: str, keywords: list[str] | None = None
-    ) -> float:
-        """计算兴趣匹配度（使用智能兴趣匹配系统，带超时保护）"""
-
-        # 调试日志：检查各个条件
-        if not content:
-            logger.debug("兴趣匹配返回0.0: 内容为空")
-            return 0.0
-        if not self.use_smart_matching:
-            logger.debug("兴趣匹配返回0.0: 智能匹配未启用")
-            return 0.0
-        if not bot_interest_manager.is_initialized:
-            logger.debug("兴趣匹配返回0.0: bot_interest_manager未初始化")
-            return 0.0
-
-        logger.debug(f"开始兴趣匹配计算，内容: {content[:50]}...")
-
-        try:
-            # 使用机器人的兴趣标签系统进行智能匹配（5秒超时保护）
-            match_result = await asyncio.wait_for(
-                bot_interest_manager.calculate_interest_match(
-                    content, keywords or [], getattr(message, "semantic_embedding", None)
-                ),
-                timeout=5.0
-            )
-            logger.debug(f"兴趣匹配结果: {match_result}")
-
-            if match_result:
-                # 返回匹配分数，考虑置信度和匹配标签数量
-                affinity_config = global_config.affinity_flow
-                match_count_bonus = min(
-                    len(match_result.matched_tags) * affinity_config.match_count_bonus, affinity_config.max_match_bonus
-                )
-                final_score = match_result.overall_score * 1.15 * match_result.confidence + match_count_bonus
-                # 移除兴趣匹配分数上限，允许超过1.0，最终分数会被整体限制
-                logger.debug(f"兴趣匹配最终得分: {final_score:.3f} (原始: {match_result.overall_score * 1.15 * match_result.confidence + match_count_bonus:.3f})")
-                return final_score
-            else:
-                logger.debug("兴趣匹配返回0.0: match_result为None")
-                return 0.0
-
-        except asyncio.TimeoutError:
-            logger.warning("[超时] 兴趣匹配计算超时(>5秒)，返回默认分值0.5以保留其他分数")
-            return 0.5  # 超时时返回默认分值，避免丢失提及分和关系分
-        except Exception as e:
-            logger.warning(f"智能兴趣匹配失败: {e}")
-            return 0.0
-
     async def _calculate_relationship_score(self, user_id: str) -> float:
         """计算用户关系分"""
         if not user_id:
@@ -316,60 +272,167 @@ class AffinityInterestCalculator(BaseInterestCalculator):
 
         return adjusted_reply_threshold, adjusted_action_threshold
 
-    def _extract_keywords_from_database(self, message: "DatabaseMessages") -> list[str]:
-        """从数据库消息中提取关键词"""
-        keywords = []
+    async def _initialize_semantic_scorer(self):
+        """异步初始化语义兴趣度评分器"""
+        if not self.use_semantic_scoring:
+            logger.debug("[语义评分] 未启用语义兴趣度评分")
+            return
 
-        # 尝试从 key_words 字段提取（存储的是JSON字符串）
-        key_words = getattr(message, "key_words", "")
-        if key_words:
+        try:
+            from src.chat.semantic_interest import SemanticInterestScorer
+            from src.chat.semantic_interest.runtime_scorer import ModelManager
+
+            # 查找最新的模型文件
+            model_dir = Path("data/semantic_interest/models")
+            if not model_dir.exists():
+                logger.warning(f"[语义评分] 模型目录不存在，已创建: {model_dir}")
+                model_dir.mkdir(parents=True, exist_ok=True)
+
+            # 使用模型管理器（支持人设感知）
+            self.model_manager = ModelManager(model_dir)
+            
+            # 获取人设信息
+            persona_info = self._get_current_persona_info()
+            
+            # 加载模型（自动选择合适的版本）
             try:
-                extracted = orjson.loads(key_words)
-                if isinstance(extracted, list):
-                    keywords = extracted
-            except (orjson.JSONDecodeError, TypeError):
-                keywords = []
+                scorer = await self.model_manager.load_model(
+                    version="auto",  # 自动选择或训练
+                    persona_info=persona_info
+                )
+                self.semantic_scorer = scorer
+                logger.info("[语义评分] 语义兴趣度评分器初始化成功（人设感知）")
                 
-        # 如果没有 keywords，尝试从 key_words_lite 提取
-        if not keywords:
-            key_words_lite = getattr(message, "key_words_lite", "")
-            if key_words_lite:
-                try:
-                    extracted = orjson.loads(key_words_lite)
-                    if isinstance(extracted, list):
-                        keywords = extracted
-                except (orjson.JSONDecodeError, TypeError):
-                    keywords = []
+                # 启动自动训练任务（每24小时检查一次）
+                await self.model_manager.start_auto_training(
+                    persona_info=persona_info,
+                    interval_hours=24
+                )
                 
-        # 如果还是没有，从消息内容中提取（降级方案）
-        if not keywords:
-            content = getattr(message, "processed_plain_text", "") or ""
-            keywords = self._extract_keywords_from_content(content)
+            except FileNotFoundError:
+                logger.warning(f"[语义评分] 未找到训练模型，将自动训练...")
+                # 触发首次训练
+                from src.chat.semantic_interest.auto_trainer import get_auto_trainer
+                auto_trainer = get_auto_trainer()
+                trained, model_path = await auto_trainer.auto_train_if_needed(
+                    persona_info=persona_info,
+                    force=True  # 强制训练
+                )
+                if trained and model_path:
+                    self.semantic_scorer = SemanticInterestScorer(model_path)
+                    self.semantic_scorer.load()
+                    logger.info("[语义评分] 首次训练完成，模型已加载")
+                else:
+                    logger.error("[语义评分] 首次训练失败")
+                    self.use_semantic_scoring = False
 
-        return keywords[:15]  # 返回前15个关键词
+        except ImportError:
+            logger.warning("[语义评分] 无法导入语义兴趣度模块，将禁用语义评分")
+            self.use_semantic_scoring = False
+        except Exception as e:
+            logger.error(f"[语义评分] 初始化失败: {e}")
+            self.use_semantic_scoring = False
 
-    def _extract_keywords_from_content(self, content: str) -> list[str]:
-        """从内容中提取关键词（降级方案）"""
-        import re
+    def _get_current_persona_info(self) -> dict[str, Any]:
+        """获取当前人设信息
         
-        # 清理文本
-        content = re.sub(r"[^\w\s\u4e00-\u9fff]", " ", content)  # 保留中文、英文、数字
-        words = content.split()
+        Returns:
+            人设信息字典
+        """
+        # 默认信息（至少包含名字）
+        persona_info = {
+            "name": global_config.bot.nickname,
+            "interests": [],
+            "dislikes": [],
+            "personality": "",
+        }
 
-        # 过滤和关键词提取
-        keywords = []
-        for word in words:
-            word = word.strip()
-            if (
-                len(word) >= 2  # 至少2个字符
-                and word.isalnum()  # 字母数字
-                and not word.isdigit()
-            ):  # 不是纯数字
-                keywords.append(word.lower())
+        # 优先从已生成的人设文件获取（Individuality 初始化时会生成）
+        try:
+            persona_file = Path("data/personality/personality_data.json")
+            if persona_file.exists():
+                data = orjson.loads(persona_file.read_bytes())
+                personality_parts = [data.get("personality", ""), data.get("identity", "")]
+                persona_info["personality"] = "，".join([p for p in personality_parts if p]).strip("，")
+                if persona_info["personality"]:
+                    return persona_info
+        except Exception as e:
+            logger.debug(f"[语义评分] 从文件获取人设信息失败: {e}")
 
-        # 去重并限制数量
-        unique_keywords = list(set(keywords))
-        return unique_keywords[:10]  # 返回前10个唯一关键词
+        # 退化为配置中的人设描述
+        try:
+            personality_parts = []
+            personality_core = getattr(global_config.personality, "personality_core", "")
+            personality_side = getattr(global_config.personality, "personality_side", "")
+            identity = getattr(global_config.personality, "identity", "")
+
+            if personality_core:
+                personality_parts.append(personality_core)
+            if personality_side:
+                personality_parts.append(personality_side)
+            if identity:
+                personality_parts.append(identity)
+
+            persona_info["personality"] = "，".join(personality_parts) or "默认人设"
+        except Exception as e:
+            logger.debug(f"[语义评分] 使用配置获取人设信息失败: {e}")
+            persona_info["personality"] = "默认人设"
+
+        return persona_info
+
+    async def _calculate_semantic_score(self, content: str) -> float:
+        """计算语义兴趣度分数
+
+        Args:
+            content: 消息文本
+
+        Returns:
+            语义兴趣度分数 [0.0, 1.0]
+        """
+        # 检查是否启用
+        if not self.use_semantic_scoring:
+            return 0.0
+
+        # 检查评分器是否已加载
+        if not self.semantic_scorer:
+            return 0.0
+
+        # 检查内容是否为空
+        if not content or not content.strip():
+            return 0.0
+
+        try:
+            # 调用评分器（异步 + 线程池，避免CPU密集阻塞事件循环）
+            loop = asyncio.get_running_loop()
+            score = await loop.run_in_executor(None, self.semantic_scorer.score, content)
+            logger.debug(f"[语义评分] 内容: '{content[:50]}...' -> 分数: {score:.3f}")
+            return score
+
+        except Exception as e:
+            logger.warning(f"[语义评分] 计算失败: {e}")
+            return 0.0
+
+    async def reload_semantic_model(self):
+        """重新加载语义兴趣度模型（支持热更新和人设检查）"""
+        if not self.use_semantic_scoring:
+            logger.info("[语义评分] 语义评分未启用，无需重载")
+            return
+
+        logger.info("[语义评分] 开始重新加载模型...")
+        
+        # 检查人设是否变化
+        if hasattr(self, 'model_manager') and self.model_manager:
+            persona_info = self._get_current_persona_info()
+            reloaded = await self.model_manager.check_and_reload_for_persona(persona_info)
+            if reloaded:
+                self.semantic_scorer = self.model_manager.get_scorer()
+                logger.info("[语义评分] 模型重载完成（人设已更新）")
+            else:
+                logger.info("[语义评分] 人设未变化，无需重载")
+        else:
+            # 降级：简单重新初始化
+            await self._initialize_semantic_scorer()
+            logger.info("[语义评分] 模型重载完成")
 
     def update_no_reply_count(self, replied: bool):
         """更新连续不回复计数"""