feat: 垃圾信息分类标签功能

新增垃圾信息细分类标签，在朴素贝叶斯二分类基础上对spam进行细分：
- 新增 spam_categorizer.py 分类模块（诈骗/骚扰/广告）
- SpamPredictionLog 和 ContentPost 模型添加 category 字段
- content_routes 和 spam_routes 接口返回分类标签

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

backend/app/ml/spam_categorizer.py

@@ -0,0 +1,68 @@
+"""垃圾信息分类标签模块
+
+在朴素贝叶斯二分类（spam/ham）基础上，对判定为 spam 的文本进行细分类标签。
+分类优先级：诈骗 > 骚扰 > 广告（按危害程度排序）
+"""
+
+CATEGORY_KEYWORDS = {
+    "fraud": [
+        "中奖", "幸运粉丝", "幸运用户", "银行卡异常", "社保异常", "账号冻结",
+        "解封", "立即验证", "验证码", "欠费停机", "退款待确认", "违章信息",
+        "紧急通知", "账户异常", "风险", "核验", "被冻结", "将被冻结",
+    ],
+    "harassment": [
+        "兼职", "日结", "高薪", "刷单", "赚钱", "外快", "宝妈", "学生都能做",
+        "添加微信", "扫码进群", "进群立刻", "想赚", "零花钱", "在家办公",
+        "无需面试", "火热招募", "秒赚", "招募",
+    ],
+    "advertisement": [
+        "领取", "优惠", "红包", "优惠券", "秒杀", "返现", "补贴", "会员",
+        "特价", "低价", "点击链接", "扫码", "免费领取", "无门槛", "现金券",
+        "盲盒", "百分百中奖", "隐藏优惠券", "内部价", "货到付款", "限时",
+        "最后", "名额", "先到先得",
+    ],
+}
+
+CATEGORY_LABELS = {
+    "fraud": "疑似诈骗",
+    "harassment": "疑似骚扰",
+    "advertisement": "疑似广告",
+    "spam": "疑似垃圾",
+    "ham": "",
+}
+
+CATEGORY_PRIORITY = ["fraud", "harassment", "advertisement"]
+
+
+def categorize_spam(text: str) -> tuple[str, str]:
+    """根据关键词匹配判定垃圾信息的具体分类标签
+
+    Args:
+        text: 待分类的文本内容
+
+    Returns:
+        tuple[str, str]: (category_code, category_label)
+            - category_code: fraud | harassment | advertisement | spam
+            - category_label: 疑似诈骗 | 疑似骚扰 | 疑似广告 | 疑似垃圾
+    """
+    text_lower = text.lower()
+
+    for category in CATEGORY_PRIORITY:
+        keywords = CATEGORY_KEYWORDS.get(category, [])
+        for kw in keywords:
+            if kw.lower() in text_lower:
+                return category, CATEGORY_LABELS[category]
+
+    return "spam", CATEGORY_LABELS["spam"]
+
+
+def get_category_label(category: str) -> str:
+    """获取分类标签的中文显示文本
+
+    Args:
+        category: 分类代码
+
+    Returns:
+        str: 中文标签文本
+    """
+    return CATEGORY_LABELS.get(category, "")

backend/app/models.py

@@ -79,6 +79,7 @@ class SpamPredictionLog(db.Model):
     user_id = db.Column(db.Integer, db.ForeignKey("users.id"), nullable=False, index=True)
     text = db.Column(db.Text, nullable=False)
     prediction = db.Column(db.String(16), nullable=False)  # spam | ham
+    category = db.Column(db.String(32), default="")  # fraud | harassment | advertisement | spam | 空
     spam_probability = db.Column(db.Float, nullable=False)
     ham_probability = db.Column(db.Float, nullable=False)
     confidence = db.Column(db.Float, nullable=False)
@@ -92,6 +93,7 @@ class SpamPredictionLog(db.Model):
             "user_id": self.user_id,
             "text": self.text,
             "prediction": self.prediction,
+            "category": self.category or "",
             "spam_probability": round(float(self.spam_probability), 4),
             "ham_probability": round(float(self.ham_probability), 4),
             "confidence": round(float(self.confidence), 4),
@@ -130,6 +132,7 @@ class ContentPost(db.Model):
 
     status = db.Column(db.String(16), nullable=False, default="published")  # published | blocked
     prediction = db.Column(db.String(16), nullable=False, default="ham")
+    category = db.Column(db.String(32), default="")  # fraud | harassment | advertisement | spam | 空
     spam_probability = db.Column(db.Float, nullable=False, default=0)
     ham_probability = db.Column(db.Float, nullable=False, default=0)
     confidence = db.Column(db.Float, nullable=False, default=0)
@@ -163,6 +166,7 @@ class ContentPost(db.Model):
             "visibility": self.visibility,
             "status": self.status,
             "prediction": self.prediction,
+            "category": self.category or "",
             "spam_probability": round(float(self.spam_probability), 4),
             "ham_probability": round(float(self.ham_probability), 4),
             "confidence": round(float(self.confidence), 4),

backend/app/routes/content_routes.py

@@ -5,6 +5,7 @@ from flask_jwt_extended import jwt_required
 
 from app.extensions import db
 from app.ml.naive_bayes_classifier import NaiveBayesSpamClassifier
+from app.ml.spam_categorizer import categorize_spam, get_category_label
 from app.models import ContentPost, DetectionConfig, SpamPredictionLog, SpamTrainingSample, User
 from app.utils.auth import current_user
 from app.utils.response import fail, ok
@@ -77,7 +78,7 @@ def _resolve_recipient(payload: dict, visibility: str, current_user_id: int):
     return recipient, None
 
 
-def _predict_and_decide(text: str, user_credit: int = 100) -> tuple[dict, float, bool]:
+def _predict_and_decide(text: str, user_credit: int = 100) -> tuple[dict, float, bool, str, str]:
     """根据用户信誉分调整阈值系数。信誉分越高，阈值越高（降低敏感度）"""
     clf = _ensure_ready()
     result = clf.predict(text)
@@ -92,7 +93,14 @@ def _predict_and_decide(text: str, user_credit: int = 100) -> tuple[dict, float,
 
     adjusted_threshold = base_threshold * credit_factor
     blocked = float(result["spam_probability"]) >= adjusted_threshold
-    return result, adjusted_threshold, blocked
+
+    # 分类标签
+    category = ""
+    category_label = ""
+    if blocked:
+        category, category_label = categorize_spam(result["text"])
+
+    return result, adjusted_threshold, blocked, category, category_label
 
 
 @content_bp.post("/publish")
@@ -113,7 +121,7 @@ def publish_text():
     if err:
         return fail(err, 400)
 
-    result, threshold, blocked = _predict_and_decide(text, user.credit_score or 100)
+    result, threshold, blocked, category, category_label = _predict_and_decide(text, user.credit_score or 100)
 
     post = ContentPost(
         user_id=user.id,
@@ -122,6 +130,7 @@ def publish_text():
         visibility=visibility,
         status="blocked" if blocked else "published",
         prediction=result["prediction"],
+        category=category,
         spam_probability=result["spam_probability"],
         ham_probability=result["ham_probability"],
         confidence=result["confidence"],
@@ -135,6 +144,7 @@ def publish_text():
         user_id=user.id,
         text=result["text"],
         prediction=result["prediction"],
+        category=category,
         spam_probability=result["spam_probability"],
         ham_probability=result["ham_probability"],
         confidence=result["confidence"],
@@ -153,14 +163,18 @@ def publish_text():
 
     db.session.commit()
 
-    feedback = "发布成功" if not blocked else "疑似垃圾信息，系统已拦截，可提交申诉"
+    feedback = "发布成功" if not blocked else f"{category_label or '疑似垃圾信息'}，系统已拦截，可提交申诉"
     return ok(
         {
             "publish_allowed": not blocked,
             "action": "published" if not blocked else "blocked",
             "feedback": feedback,
             "post": _serialize_post(post),
-            "detect": result,
+            "detect": {
+                **result,
+                "category": category,
+                "category_label": category_label,
+            },
         },
         feedback,
     )
@@ -188,13 +202,14 @@ def edit_post(post_id: int):
     if err:
         return fail(err, 400)
 
-    result, threshold, blocked = _predict_and_decide(text, user.credit_score or 100)
+    result, threshold, blocked, category, category_label = _predict_and_decide(text, user.credit_score or 100)
 
     post.text = result["text"]
     post.visibility = visibility
     post.recipient_user_id = recipient.id if recipient else None
     post.status = "blocked" if blocked else "published"
     post.prediction = result["prediction"]
+    post.category = category
     post.spam_probability = result["spam_probability"]
     post.ham_probability = result["ham_probability"]
     post.confidence = result["confidence"]

backend/app/routes/spam_routes.py

@@ -3,6 +3,7 @@ from flask_jwt_extended import jwt_required
 
 from app.extensions import db
 from app.ml.naive_bayes_classifier import NaiveBayesSpamClassifier
+from app.ml.spam_categorizer import categorize_spam, get_category_label
 from app.models import DetectionConfig, SpamPredictionLog, SpamTrainingSample
 from app.utils.auth import admin_required, current_user
 from app.utils.response import fail, ok
@@ -58,10 +59,17 @@ def predict_one():
     threshold = _adjusted_threshold(user.credit_score or 100)
     blocked = float(result["spam_probability"]) >= threshold
 
+    # 分类标签：仅在判定为垃圾时进行细分
+    category = ""
+    category_label = ""
+    if blocked:
+        category, category_label = categorize_spam(result["text"])
+
     row = SpamPredictionLog(
         user_id=user.id,
         text=result["text"],
         prediction=result["prediction"],
+        category=category,
         spam_probability=result["spam_probability"],
         ham_probability=result["ham_probability"],
         confidence=result["confidence"],
@@ -71,7 +79,14 @@ def predict_one():
     db.session.add(row)
     db.session.commit()
 
-    return ok({**result, "log_id": row.id, "threshold": threshold, "blocked_by_threshold": blocked}, "识别成功")
+    return ok({
+        **result,
+        "log_id": row.id,
+        "threshold": threshold,
+        "blocked_by_threshold": blocked,
+        "category": category,
+        "category_label": category_label,
+    }, "识别成功")
 
 
 @spam_bp.post("/predict/batch")
@@ -98,12 +113,23 @@ def predict_batch():
         if len(content) < 2:
             continue
         result = clf.predict(content)
-        result["blocked_by_threshold"] = float(result["spam_probability"]) >= threshold
+        blocked = float(result["spam_probability"]) >= threshold
+        result["blocked_by_threshold"] = blocked
+
+        # 分类标签
+        category = ""
+        category_label = ""
+        if blocked:
+            category, category_label = categorize_spam(result["text"])
+        result["category"] = category
+        result["category_label"] = category_label
+
         rows.append(
             SpamPredictionLog(
                 user_id=user.id,
                 text=result["text"],
                 prediction=result["prediction"],
+                category=category,
                 spam_probability=result["spam_probability"],
                 ham_probability=result["ham_probability"],
                 confidence=result["confidence"],

backend/sql/add_category_field.sql

@@ -0,0 +1,5 @@
+-- 添加 category 字段到 spam_prediction_logs 表
+ALTER TABLE `spam_prediction_logs` ADD COLUMN `category` VARCHAR(32) DEFAULT '' AFTER `prediction`;
+
+-- 添加 category 字段到 content_posts 表
+ALTER TABLE `content_posts` ADD COLUMN `category` VARCHAR(32) DEFAULT '' AFTER `prediction`;