diff --git a/core/mmr.py b/core/mmr.py
index 7e8c29e..4565fd1 100644
--- a/core/mmr.py
+++ b/core/mmr.py
@@ -108,22 +108,44 @@ def mmr_rerank(
     return selected
 
 
+def _tokenize_words(text: str) -> set:
+    """
+    使用 jieba 分词并过滤噪声，返回有意义的词集合。
+
+    过滤规则：
+    - 去除单字符词（如 "的", "了", "在"）—— 这些是停用词，对区分文档无意义
+    - 去除纯数字 / 纯标点
+    - 保留 2 字及以上的实词
+    """
+    import jieba
+    words = set()
+    for w in jieba.cut(text):
+        w = w.strip()
+        if len(w) >= 2 and not w.isdigit():
+            words.add(w)
+    return words
+
+
 def mmr_filter_by_content(
     candidates: List[Dict],
     top_k: int = 30,
-    similarity_threshold: float = 0.9
+    similarity_threshold: float = 0.85
 ) -> List[Dict]:
     """
-    基于内容相似度的去重（简化版，不需要 embedding）
+    基于 jieba 词级 Jaccard 相似度的去重（不需要 embedding）
+
+    与旧版字符级 set(text) 的区别：
+    - 旧版：set("安全生产管理制度") → {'安','全','生','产',...}，中文文档间字符集合高度重叠
+    - 新版：jieba 分词 → {"安全生产", "管理制度", ...}，词级集合区分度高
 
     适用于：
-    - 没有 embedding 的情况
-    - 快速去重场景
+    - MMR_USE_EMBEDDING=False 时的快速去重
+    - 避免 CPU 编码 100+ 文档的 50 秒开销
 
     Args:
         candidates: 候选文档列表
         top_k: 返回数量
-        similarity_threshold: 相似度阈值，超过则视为重复
+        similarity_threshold: 相似度阈值，超过则视为重复（默认 0.85）
 
     Returns:
         去重后的候选文档列表
@@ -134,35 +156,42 @@ def mmr_filter_by_content(
     if len(candidates) <= top_k:
         return candidates
 
-    selected = []
-    remaining = candidates.copy()
+    # 预分词：对所有候选文档一次性分词，避免重复调用 jieba.cut
+    word_sets = []
+    for c in candidates:
+        content = c.get('content', c.get('document', ''))[:500]
+        word_sets.append(_tokenize_words(content))
 
-    while len(selected) < top_k and remaining:
-        current = remaining.pop(0)
+    selected_indices = []
+
+    for i in range(len(candidates)):
+        if len(selected_indices) >= top_k:
+            break
+
+        current_words = word_sets[i]
+        if not current_words:
+            # 空内容直接保留
+            selected_indices.append(i)
+            continue
 
-        # 检查是否与已选内容重复
         is_duplicate = False
-        current_content = current.get('content', current.get('document', ''))[:200]
+        for j in selected_indices:
+            selected_words = word_sets[j]
+            if not selected_words:
+                continue
 
-        for s in selected:
-            s_content = s.get('content', s.get('document', ''))[:200]
+            intersection = len(current_words & selected_words)
+            union = len(current_words | selected_words)
+            similarity = intersection / union if union > 0 else 0
 
-            # 简单的 Jaccard 相似度
-            words1 = set(current_content)
-            words2 = set(s_content)
-            if words1 and words2:
-                intersection = len(words1 & words2)
-                union = len(words1 | words2)
-                similarity = intersection / union if union > 0 else 0
-
-                if similarity > similarity_threshold:
-                    is_duplicate = True
-                    break
+            if similarity > similarity_threshold:
+                is_duplicate = True
+                break
 
         if not is_duplicate:
-            selected.append(current)
+            selected_indices.append(i)
 
-    return selected
+    return [candidates[i] for i in selected_indices]
 
 
 # ==================== 测试 ====================