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

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+"""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, "")