diff --git a/Cargo.toml b/Cargo.toml
index bd724e1..bc021d4 100644
--- a/Cargo.toml
+++ b/Cargo.toml
@@ -21,4 +21,5 @@ tracing-subscriber = "0.3"
 thiserror = "1.0"
 mimalloc = "0.1.48"
 regex = "1"
-lazy_static = "1.4"
\ No newline at end of file
+lazy_static = "1.4"
+jieba-rs = "0.7"
\ No newline at end of file
diff --git a/src/engine/pipeline.rs b/src/engine/pipeline.rs
index 748f64a..b7845d4 100644
--- a/src/engine/pipeline.rs
+++ b/src/engine/pipeline.rs
@@ -4,16 +4,15 @@ use crate::db::Database;
 use crate::engine::title_parser;
 use crate::engine::tag_extractor;
 use crate::engine::person_merger;
-use std::collections::HashMap;
 
-/// 处理所有相册的元数据：提取人名 + 标签
-/// 两阶段：先用正则提取人名入库，再用匹配精炼，最后提取标签
+/// 处理所有相册的元数据：提取人名 + 标签（单次 jieba 分词）
+/// 一阶段：一次调用 extract_person_and_tags 同时获取人名和标签
 pub async fn process_all_albums(db: &Database) -> (usize, usize) {
     let albums = db.get_all_albums_full().await;
     let mut person_count = 0;
     let mut tag_count = 0;
 
-    // Phase 1: 先用正则从标题直接提取人名（冷启动，不需要已有 persons）
+    // 已有人物库时优先用匹配（更准确）
     let patterns = person_merger::collect_person_patterns(db).await;
     let use_patterns = !patterns.is_empty();
 
@@ -21,14 +20,26 @@ pub async fn process_all_albums(db: &Database) -> (usize, usize) {
         let title = album.title.as_deref().unwrap_or("");
         if title.is_empty() { continue; }
 
-        // 提取人名
-        if album.person_name_original.is_none() || album.person_name_original.as_deref() == Some("") {
+        // 判断是否需要处理
+        let need_person = album.person_name_original.is_none()
+            || album.person_name_original.as_deref() == Some("");
+        let need_tags = album.tags.as_deref().unwrap_or("").is_empty()
+            || album.tags.as_deref() == Some("[]");
+
+        if !need_person && !need_tags { continue; }
+
+        // 一次 jieba 分词，同时获取人名 + 标签
+        let (person_from_jieba, tags_from_jieba) = title_parser::extract_person_and_tags(title);
+
+        // ── 处理人名 ──
+        if need_person {
             let person_name = if use_patterns {
-                // 已有人物库，用匹配
-                person_merger::match_title(title, &patterns).map(|(_, name, _)| name)
+                // 已有人物库，优先用匹配；匹配不到再回退到 jieba
+                person_merger::match_title(title, &patterns)
+                    .map(|(_, name, _)| name)
+                    .or(person_from_jieba)
             } else {
-                // 冷启动，直接用正则提取
-                title_parser::extract_person_name_from_title(title)
+                person_from_jieba
             };
 
             if let Some(person_name) = person_name {
@@ -52,27 +63,17 @@ pub async fn process_all_albums(db: &Database) -> (usize, usize) {
                 person_count += 1;
             }
         }
-    }
 
-    // Phase 2: 提取标签（使用已填充的人名来排除）
-    // 重新获取 albums（因为 person_name_cleaned 已更新）
-    let albums_refreshed = db.get_all_albums_full().await;
-    let mut seen_albums: HashMap<String, String> = HashMap::new();
-    for album in &albums {
-        if let Some(pn) = &album.person_name_cleaned {
-            seen_albums.insert(album.id.clone(), pn.clone());
-        }
-    }
-
-    for album in &albums_refreshed {
-        let title = album.title.as_deref().unwrap_or("");
-        if title.is_empty() { continue; }
-
-        let existing_tags = album.tags.as_deref().unwrap_or("");
-        if existing_tags.is_empty() || existing_tags == "[]" {
+        // ── 处理标签 ──
+        if need_tags {
+            // 用当前 album 的人名（优先已有的，否则用刚提取的）
             let person = album.person_name_cleaned.as_deref()
-                .or_else(|| seen_albums.get(&album.id).map(|s| s.as_str()));
-            let tags = tag_extractor::extract_tags_from_title(title, person);
+                .or_else(|| album.person_name_original.as_deref());
+
+            // 结合分类词库匹配（高质量标签）+ jieba 名词提取
+            let mut tags = tag_extractor::extract_tags_from_title(title, person);
+            tags.extend(tags_from_jieba);
+
             let unique = tag_extractor::unique_tags(tags);
             if !unique.is_empty() {
                 let tag_names: Vec<String> = unique.iter().map(|(t, _)| t.clone()).collect();
@@ -87,4 +88,4 @@ pub async fn process_all_albums(db: &Database) -> (usize, usize) {
 
     println!("[管线] 处理完成：{} 个人名，{} 个标签", person_count, tag_count);
     (person_count, tag_count)
-}
\ No newline at end of file
+}
diff --git a/src/engine/tag_extractor.rs b/src/engine/tag_extractor.rs
index 4b04060..0c601f0 100644
--- a/src/engine/tag_extractor.rs
+++ b/src/engine/tag_extractor.rs
@@ -12,6 +12,12 @@
 use regex::Regex;
 use lazy_static::lazy_static;
 use std::collections::HashSet;
+use jieba_rs::Jieba;
+use crate::engine::title_parser;
+
+lazy_static! {
+    static ref JIEBA: Jieba = Jieba::new();
+}
 
 lazy_static! {
     static ref WORD_RE: Regex = Regex::new(r"[a-zA-Z0-9\u{4e00}-\u{9fff}]+").unwrap();
@@ -122,6 +128,94 @@ fn classify_tag(word: &str) -> &'static str {
 
 // ── 核心函数 ──
 
+/// 用 jieba 分词提取名词标签（排除人名）
+/// 提取词性为 n(名词)、nz(其它专名)、ns(地名)、vn(名动词) 的词
+/// 但排除 nr(人名) 类，因为人名已单独提取
+fn extract_tags_with_jieba(
+    title: &str,
+    person_name: Option<&str>,
+    stop_words: &HashSet<&'static str>,
+    seen: &HashSet<String>,
+) -> Vec<(String, String)> {
+    let mut tags = Vec::new();
+
+    // 对标题做词性标注分词
+    let jieba_tags = JIEBA.tag(title, true);
+
+    // 人名排除集合（小写）
+    let person_exclude: Vec<String> = person_name
+        .map(|n| {
+            let mut v = vec![n.to_lowercase()];
+            for part in n.split_whitespace() {
+                v.push(part.to_lowercase());
+            }
+            // 也提取中文核心名用于排除
+            if let Some(cn) = title_parser::extract_cn(n) {
+                v.push(cn.to_lowercase());
+            }
+            v
+        })
+        .unwrap_or_default();
+
+    for tag in &jieba_tags {
+        let word = tag.word.trim();
+        if word.len() < 2 { continue; }
+
+        let word_lower = word.to_lowercase();
+
+        // 排除纯数字
+        if word.chars().all(|c| c.is_ascii_digit()) { continue; }
+
+        // 排除停用词
+        if stop_words.contains(word_lower.as_str()) { continue; }
+
+        // 排除人名
+        if person_exclude.iter().any(|p| word_lower.contains(p)) { continue; }
+
+        // 排除已经匹配过的
+        if seen.contains(&word_lower) { continue; }
+
+        // 按词性过滤：只保留名词相关，但排除人名(nr)
+        let pos = tag.tag.as_str();
+        let category = match pos {
+            // 名词类（排除人名）
+            "n" | "ng" => "noun",        // 普通名词
+            "nz" => "proper",             // 其它专名
+            "ns" => "location",           // 地名
+            "nt" => "organization",       // 机构团体名
+            "vn" => "noun_verb",          // 名动词
+            "a" | "ad" | "an" => "adj",  // 形容词
+            "v" | "vd" | "vg" => "verb", // 动词
+            "eng" => "english",           // 英文词
+            "x" => "other",               // 其它
+            // 排除的词性
+            "nr" | "nrfg" | "nrt" => continue, // 人名类跳过
+            "m" | "mq" => continue,       // 数词
+            "q" | "qv" | "qt" => continue, // 量词
+            "r" | "rr" | "rz" => continue, // 代词
+            "c" => continue,              // 连词
+            "p" => continue,              // 介词
+            "u" | "ul" | "uv" | "ud" | "ug" | "uj" => continue, // 助词
+            "d" => continue,              // 副词
+            "e" | "y" | "o" => continue,  // 叹词/语气词/拟声词
+            "w" => continue,              // 标点
+            "t" | "tg" => continue,       // 时间词
+            "s" => continue,              // 处所词
+            "f" => continue,              // 方位词
+            "b" | "bl" => continue,       // 区别词
+            "z" => continue,              // 状态词
+            _ => continue,
+        };
+
+        // 进一步过滤：太短的英文词或太通用的词
+        if category == "english" && word.len() < 3 { continue; }
+
+        tags.push((word.to_string(), category.to_string()));
+    }
+
+    tags
+}
+
 /// 从标题中提取分类标签
 pub fn extract_tags_from_title(
     title: &str,
@@ -154,7 +248,17 @@ pub fn extract_tags_from_title(
         }
     }
 
-    // 第二步：分词提取其他关键词
+    // 第二步：用 jieba 分词提取名词标签（比正则更准确）
+    let jieba_tags = extract_tags_with_jieba(title, person_name, &stop_words, &seen);
+    for (tag, category) in jieba_tags {
+        let key = tag.to_lowercase();
+        if !seen.contains(&key) {
+            seen.insert(key);
+            tags.push((tag, category));
+        }
+    }
+
+    // 第三步：正则分词提取其他关键词（兜底）
     for cap in WORD_RE.find_iter(&title_lower) {
         let word = cap.as_str().trim();
         let word_lower = word.to_lowercase();
@@ -195,9 +299,11 @@ pub fn unique_tags(tags: Vec<(String, String)>) -> Vec<(String, String)> {
     let mut seen = HashSet::new();
     let mut result = Vec::new();
 
-    // 分类排序权重
+    // 分类排序权重：预设词库分类最优先，然后是 jieba 提取的名词类
     let category_order: Vec<&str> = vec![
-        "style", "outfit", "scene", "body", "mood", "topic", "chinese", "english", "other"
+        "style", "outfit", "scene", "body", "mood", "topic",
+        "noun", "proper", "location", "organization", "noun_verb",
+        "adj", "verb", "chinese", "english", "other"
     ];
 
     let mut sorted_tags = tags;
@@ -225,6 +331,13 @@ pub fn get_category_label(category: &str) -> &'static str {
         "body" => "身体",
         "mood" => "情绪",
         "topic" => "主题",
+        "noun" => "名词",
+        "proper" => "专名",
+        "location" => "地名",
+        "organization" => "机构",
+        "noun_verb" => "名动",
+        "adj" => "形容词",
+        "verb" => "动词",
         "chinese" => "中文",
         "english" => "英文",
         _ => "其他",
@@ -240,6 +353,13 @@ pub fn get_category_color(category: &str) -> (&'static str, &'static str) {
         "body"    => ("#fef3c7", "#b45309"),   // 浅黄底 + 深黄字
         "mood"    => ("#faf5ff", "#7c3aed"),   // 浅紫底 + 深紫字
         "topic"   => ("#f0f9ff", "#0369a1"),   // 浅蓝底 + 蓝字
+        "noun"    => ("#eff6ff", "#1d4ed8"),   // 蓝灰底 + 深蓝字
+        "proper"  => ("#e0e7ff", "#4338ca"),   // 靛蓝底 + 深靛字
+        "location"=> ("#ecfdf5", "#047857"),   // 翠绿底 + 深绿字
+        "organization" => ("#fef9c3", "#a16207"), // 浅黄底 + 暗黄字
+        "noun_verb" => ("#fce7f3", "#be185d"), // 浅粉底 + 深粉字
+        "adj"     => ("#f3e8ff", "#6d28d9"),   // 浅紫底 + 深紫字
+        "verb"    => ("#fed7aa", "#c2410c"),   // 橙底 + 深橙字
         "chinese" => ("#f1f5f9", "#475569"),   // 浅灰底 + 深灰字
         "english" => ("#f1f5f9", "#64748b"),   // 浅灰底 + 灰字
         _         => ("#f1f5f9", "#64748b"),   // 默认
diff --git a/src/engine/title_parser.rs b/src/engine/title_parser.rs
index 5e367ed..781ed73 100644
--- a/src/engine/title_parser.rs
+++ b/src/engine/title_parser.rs
@@ -4,6 +4,12 @@
 use regex::Regex;
 use std::collections::HashSet;
 use lazy_static::lazy_static;
+use jieba_rs::Jieba;
+
+// 全局 jieba 分词器
+lazy_static! {
+    static ref JIEBA: Jieba = Jieba::new();
+}
 
 // ── 常量 ──
 
@@ -30,11 +36,48 @@ const STOP_WORDS: &[&str] = &[
 /// 保留词性（仅用于 jieba 分词场景，这里做占位符）
 const _KEEP_POS: &[&str] = &["n", "nr", "ns", "nz", "v", "vn", "a", "an", "eng", "x"];
 
-/// 工作室/平台排除名
+/// 工作室/平台排除名（大幅扩充）
 const EXCLUDED_NAMES: &[&str] = &[
+    // 工作室/平台
     "Pure Media", "XiuRen秀人网", "尤蜜", "魅妍社", "花漾",
     "瑞丝馆", "撸图", "情欲萝莉", "蜜桃社", "tgod",
     "MiStar", "BLUECAKE", "HuaYang", "YouMi", "秀人",
+    "XIUREN", "YouMi尤蜜", "IMiss爱蜜社", "MiiTao蜜桃社",
+    "MFStar模范学院", "TGOD推女神", "DKGirl御女郎",
+    "XINGYAN星颜社", "XIAOYU语画界", "YOUMI尤蜜荟",
+    "FAIRY CLUB", "FAIRY-CLUB", "FEILIN嗲囡囡",
+    "CANDY糖果画报", "YouWu尤物馆", "UXING优星馆",
+    "108TV", "108酱", "RuiSG瑞丝馆", "SIW斯文传媒",
+    "SLADY猎女神", "SSA丝社", "SJA佳爷", "TASHA系列",
+    "Ugirls爱尤物", "V Girls", "VGirl", "WingS影私荟",
+    "YALAYI雅拉伊", "ISHOW爱秀", "ISHOW",
+    "写真", "私房", "套图", "系列", "合集", "作品",
+    // 国家/地区
+    "日本", "韩国", "中国", "台湾", "香港", "泰国", "越南",
+    "新加坡", "马来西亚", "菲律宾", "印度尼西亚", "美国", "俄罗斯",
+    "东京", "首尔", "北京", "上海", "广州", "深圳", "成都", "重庆",
+    "曼谷", "胡志明", "新加坡市", "马尼拉", "雅加达",
+    // 非人名常见词
+    "模特", "美女", "女神", "小姐姐", "小仙女", "御姐", "萝莉",
+    "性感", "诱惑", "清纯", "甜美", "可爱", "迷人", "魅力",
+    "时尚", "潮流", "高清", "原版", "无水印", "完整版",
+    "比基尼", "泳装", "内衣", "丝袜", "制服", "旗袍", "睡衣",
+    "浴室", "浴缸", "床上", "沙发", "厨房", "阳台",
+    "室内", "户外", "街拍", "旅拍", "海边", "沙滩", "泳池",
+    "cosplay", "角色扮演", "动漫", "游戏",
+    "超短裙", "包臀裙", "紧身裙", "连衣裙", "牛仔裤", "热裤",
+    "高跟鞋", "运动鞋", "凉鞋",
+    "美腿", "美胸", "翘臀", "玉足", "美背", "香肩", "蛮腰",
+    "白皙", "肌肤", "纹身",
+    "日系", "韩系", "森系", "复古", "冷艳", "优雅", "知性",
+    "俏皮", "温柔", "妩媚", "野性", "热情", "慵懒", "迷离",
+    "唯美", "清新", "冷傲",
+    // 工作室前缀
+    "PureMedia", "BlueCake", "HuaYang", "XiuRen", "MiStar",
+    "YouMi", "MFStar", "TGOD", "DKGirl", "XINGYAN", "XIAOYU",
+    "YOUMI", "FAIRYCLUB", "FEILIN", "CANDY", "YouWu", "UXING",
+    "RuiSG", "SIW", "SLADY", "SSA", "SJA", "TASHA", "Ugirls",
+    "VGIRLS", "VGIRL", "WING", "YALAYI", "ISHOW",
 ];
 
 lazy_static! {
@@ -81,11 +124,13 @@ lazy_static! {
 // ── 人名提取 ──
 
 /// 从标题中提取模特/人物名称
-/// 核心策略：
-/// 1. 清理标题（移除[工作室]、编号、N photos等）
-/// 2. 尝试用分隔符(–—-)切分，第一部分常含人名
-/// 3. 扫描匹配"2-5中文+可选英文"模式的词
-/// 4. 结合上下文（后面是否跟"写真/私房/性感"等关键词）
+/// 核心策略（按优先级）：
+/// 1. cosplay 格式匹配（Name cosplay Character）
+/// 2. jieba 分词 + 词性标注（优先提取 nr 人名）
+/// 3. 清理标题（移除[工作室]、编号、N photos等）
+/// 4. 用分隔符(–—-)切分，第一部分常含人名
+/// 5. 扫描匹配"2-5中文+可选英文"模式的词
+/// 6. 结合上下文（后面是否跟"写真/私房/性感"等关键词）
 pub fn extract_person_name_from_title(title: &str) -> Option<String> {
     let title = title.trim();
     if title.is_empty() {
@@ -103,6 +148,12 @@ pub fn extract_person_name_from_title(title: &str) -> Option<String> {
         return Some(clean_name(caps[1].trim()));
     }
 
+    // 步骤2.5: jieba 分词 + 词性标注（最高优先级的中文人名提取）
+    // 只保留词性为 nr(人名)、n(名词)、nz(其它专名) 的词
+    if let Some(name) = extract_person_with_jieba(&t) {
+        return Some(name);
+    }
+
     // 步骤3: 移除 [工作室] 前缀
     t = RE_BRACKET_PREFIX.replace(&t, "").trim().to_string();
 
@@ -123,7 +174,7 @@ pub fn extract_person_name_from_title(title: &str) -> Option<String> {
     // 策略A: 检查第一部分（分隔符前的内容最可能包含人名）
     if let Some(first) = parts.first() {
         if let Some(name) = extract_name_from_segment(first) {
-            if !is_excluded_name(&name) {
+            if is_likely_person_name(&name) {
                 return Some(name);
             }
         }
@@ -141,7 +192,7 @@ pub fn extract_person_name_from_title(title: &str) -> Option<String> {
         };
 
         if let Some(name) = extract_name_from_segment(part) {
-            if !is_excluded_name(&name) {
+            if is_likely_person_name(&name) {
                 if has_context_after {
                     return Some(name); // 有关键词上下文，优先返回
                 }
@@ -152,7 +203,7 @@ pub fn extract_person_name_from_title(title: &str) -> Option<String> {
     // 策略C: 直接在整个标题中搜索人名模式
     for cap in RE_CN_NAME.captures_iter(&t) {
         let name = clean_name(&cap[1]);
-        if !is_excluded_name(&name) {
+        if is_likely_person_name(&name) {
             return Some(name);
         }
     }
@@ -160,6 +211,39 @@ pub fn extract_person_name_from_title(title: &str) -> Option<String> {
     None
 }
 
+/// 用 jieba 分词提取人名（最高优先级策略）
+/// 只保留词性为 nr(人名)、n(名词)、nz(其它专名) 的词
+/// 同时结合二次验证过滤非人名
+fn extract_person_with_jieba(title: &str) -> Option<String> {
+    // 对标题做词性标注分词（true = 使用 HMM 新词发现）
+    let tags = JIEBA.tag(title, true);
+
+    // 按优先级收集候选词：nr > nz > n
+    let mut candidates: Vec<(String, usize)> = Vec::new(); // (词, 优先级)
+
+    for tag in &tags {
+        let word = tag.word.trim();
+        if word.len() < 2 { continue; }
+
+        let priority = match tag.tag.as_str() {
+            "nr" => 0,    // 人名（最高优先级）
+            "nrfg" => 1,  // 古体人名
+            "nrt" => 2,   // 音译人名
+            "nz" => 3,    // 其它专名
+            "n" => 4,     // 名词
+            _ => continue, // 其他词性跳过
+        };
+
+        if is_likely_person_name(word) {
+            candidates.push((word.to_string(), priority));
+        }
+    }
+
+    // 按优先级排序，取第一个
+    candidates.sort_by(|a, b| a.1.cmp(&b.1));
+    candidates.into_iter().next().map(|(name, _)| clean_name(&name))
+}
+
 /// 从一个文本片段中提取人名
 fn extract_name_from_segment(segment: &str) -> Option<String> {
     let seg = segment.trim();
@@ -213,6 +297,50 @@ pub fn is_excluded_name(name: &str) -> bool {
     EXCLUDED_LOWER.contains(&name.to_lowercase())
 }
 
+/// 二次验证：判断一个词是否"看起来像人名"
+/// 返回 true = 像人名，可以保留；false = 不像，应该排除
+pub fn is_likely_person_name(name: &str) -> bool {
+    let name = name.trim();
+    if name.is_empty() { return false; }
+    if name.len() < 2 { return false; }
+
+    // 先排除已知的非人名
+    if is_excluded_name(name) { return false; }
+
+    let name_lower = name.to_lowercase();
+
+    // 纯数字排除
+    if name.chars().all(|c| c.is_ascii_digit()) { return false; }
+
+    // 含数字太多的排除（超过一半是数字）
+    let digit_count = name.chars().filter(|c| c.is_ascii_digit()).count();
+    if digit_count * 2 > name.len() { return false; }
+
+    // 纯英文且长度 > 15 的排除（太长不像人名）
+    let has_cn = RE_CHINESE.is_match(name);
+    if !has_cn && name.len() > 15 { return false; }
+
+    // 以数字开头的排除（除非是纯数字的英文名）
+    if name.chars().next().map(|c| c.is_ascii_digit()).unwrap_or(false) { return false; }
+
+    // 含特殊字符过多的排除
+    let special_count = name.chars().filter(|c| {
+        !c.is_alphanumeric() && !RE_CHINESE.is_match(&c.to_string())
+    }).count();
+    if special_count > 2 { return false; }
+
+    // 已知后缀排除（如 "photos", "series", "collection" 等）
+    let known_suffix = ["photos", "series", "collection", "edition", "set", "vol", "album"];
+    for suffix in known_suffix {
+        if name_lower.ends_with(suffix) { return false; }
+    }
+
+    // 全部是大写字母且长度 > 6 的排除（通常是缩写/工作室名）
+    if name.chars().all(|c| c.is_ascii_uppercase()) && name.len() > 6 { return false; }
+
+    true
+}
+
 // ── 人名清洗 ──
 
 /// 清洗人名：移除前缀、括号内容、末尾标点
@@ -330,6 +458,139 @@ pub fn split_person_name_with_aliases(raw_name: &str) -> (String, Vec<(String, S
     (primary_name, aliases)
 }
 
+// ── 一键提取：人名 + 标签（单次 jieba 分词） ──
+
+/// 一次 jieba 分词，同时提取人名和标签
+/// 返回 (人名Option, 标签Vec)
+/// 优势：只做一次分词，省去重复开销
+pub fn extract_person_and_tags(title: &str) -> (Option<String>, Vec<(String, String)>) {
+    let title = title.trim();
+    if title.is_empty() {
+        return (None, Vec::new());
+    }
+
+    // 做一次词性标注分词
+    let tags = JIEBA.tag(title, true);
+
+    // ── 第一步：提取人名（优先级最高）──
+    let person_name = extract_person_from_tags(&tags);
+
+    // ── 第二步：提取名词标签（排除人名）──
+    let tag_list = extract_tags_from_jieba_result(&tags, person_name.as_deref());
+
+    (person_name, tag_list)
+}
+
+/// 从 jieba 分词结果中提取人名
+fn extract_person_from_tags(tags: &[jieba_rs::Token]) -> Option<String> {
+    let mut candidates: Vec<(String, usize)> = Vec::new();
+
+    for tag in tags {
+        let word = tag.word.trim();
+        if word.len() < 2 { continue; }
+
+        let priority = match tag.tag.as_str() {
+            "nr" => 0,    // 人名（最高优先级）
+            "nrfg" => 1,  // 古体人名
+            "nrt" => 2,   // 音译人名
+            "nz" => 3,    // 其它专名
+            "n" => 4,     // 名词
+            _ => continue,
+        };
+
+        if is_likely_person_name(word) {
+            candidates.push((word.to_string(), priority));
+        }
+    }
+
+    candidates.sort_by(|a, b| a.1.cmp(&b.1));
+    candidates.into_iter().next().map(|(name, _)| clean_name(&name))
+}
+
+/// 从 jieba 分词结果中提取标签（排除人名）
+fn extract_tags_from_jieba_result(
+    tags: &[jieba_rs::Token],
+    person_name: Option<&str>,
+) -> Vec<(String, String)> {
+    use std::collections::HashSet;
+
+    let mut result = Vec::new();
+    let mut seen = HashSet::new();
+
+    // 停用词
+    let stop_words: HashSet<&str> = STOP_WORDS.iter().copied().collect();
+
+    // 人名排除
+    let person_exclude: Vec<String> = person_name
+        .map(|n| {
+            let mut v = vec![n.to_lowercase()];
+            for part in n.split_whitespace() {
+                v.push(part.to_lowercase());
+            }
+            if let Some(cn) = extract_cn(n) {
+                v.push(cn.to_lowercase());
+            }
+            v
+        })
+        .unwrap_or_default();
+
+    for tag in tags {
+        let word = tag.word.trim();
+        if word.len() < 2 { continue; }
+
+        let word_lower = word.to_lowercase();
+
+        // 纯数字跳过
+        if word.chars().all(|c| c.is_ascii_digit()) { continue; }
+
+        // 停用词跳过
+        if stop_words.contains(word_lower.as_str()) { continue; }
+
+        // 人名跳过
+        if person_exclude.iter().any(|p| word_lower.contains(p)) { continue; }
+
+        // 去重
+        if !seen.insert(word_lower.clone()) { continue; }
+
+        // 按词性分类
+        let pos = tag.tag.as_str();
+        let category = match pos {
+            "n" | "ng" => "noun",
+            "nz" => "proper",
+            "ns" => "location",
+            "nt" => "organization",
+            "vn" => "noun_verb",
+            "a" | "ad" | "an" => "adj",
+            "v" | "vd" | "vg" => "verb",
+            "eng" => "english",
+            "x" => "other",
+            // 排除的词性
+            "nr" | "nrfg" | "nrt" => continue, // 人名
+            "m" | "mq" => continue,
+            "q" | "qv" | "qt" => continue,
+            "r" | "rr" | "rz" => continue,
+            "c" => continue,
+            "p" => continue,
+            "u" | "ul" | "uv" | "ud" | "ug" | "uj" => continue,
+            "d" => continue,
+            "e" | "y" | "o" => continue,
+            "w" => continue,
+            "t" | "tg" => continue,
+            "s" => continue,
+            "f" => continue,
+            "b" | "bl" => continue,
+            "z" => continue,
+            _ => continue,
+        };
+
+        if category == "english" && word.len() < 3 { continue; }
+
+        result.push((word.to_string(), category.to_string()));
+    }
+
+    result
+}
+
 // ── 标签提取 ──
 
 /// 从标题中提取标签（排除人名部分）