diff --git a/README.md b/README.md
index 58c245a..dec2753 100644
--- a/README.md
+++ b/README.md
@@ -362,28 +362,73 @@ Authorization: Bearer <your_jwt_token>
 #### 2.3 单词联想建议
 
 - **接口**: `POST /api/search/list/word`
-- **描述**: 根据用户输入返回单词联想列表，含前缀匹配与包含匹配。
+- **描述**: 返回智能联想候选列表。后端会根据 `language`（当前词典）与用户输入自动切换检索策略，综合“前缀匹配”和“释义反查”两种来源，并对结果去重合并释义。
 - **需要认证**: 是
 - **请求体**:
 
 ```json
 {
   "query": "bon",
-  "language": "fr",
-  "sort": "relevance",
-  "order": "des"
+  "language": "fr"
 }
 ```
 
-- **响应示例**:
+- **检索规则**:
+    - `language = "fr"`:
+        - 法语/拉丁字符输入：优先使用 `WordlistFr` 做前缀 + 包含匹配。
+        - 中文输入：回退到法语释义的中文字段做反查。
+        - 英文输入：会优先使用英文释义字段做反查，方便“英文 → 法语”场景。
+    - `language = "jp"`:
+        - 假名或日文汉字：直接在 `WordlistJp` 做前缀 + 包含匹配，同时返回假名字段。
+        - 中文输入：优先用中文释义反查；若该中文词条存在汉字映射，则并行检索对应的日语原词并放在结果前列。
+
+- **响应字段**:
+    - `word`: 词条原文（法语或日语）
+    - `hiragana`: 仅日语结果携带；法语为 `null`
+    - `meanings`: 中文释义去重数组（当结果来自释义反查时才会出现）
+    - `english`: 英文释义去重数组（仅法语词典且按英文释义反查时出现）
+
+- **响应示例（法语）**:
 
 ```json
 {
-  "list": ["bonjour", "bonsoir", "bonheur"]
+  "list": [
+    {
+      "word": "bonjour",
+      "hiragana": null,
+      "meanings": ["你好", "问候语"],
+      "english": ["hello"]
+    },
+    {
+      "word": "bonsoir",
+      "hiragana": null,
+      "meanings": [],
+      "english": []
+    }
+  ]
 }
 ```
 
-> **说明**: `language = "jp"` 时返回形如 `[["愛", "あい"], ["愛する", "あいする"]]` 的二维数组，第二列为假名读音。
+- **响应示例（日语，中文反查）**:
+
+```json
+{
+  "list": [
+    {
+      "word": "愛",
+      "hiragana": "あい",
+      "meanings": ["爱；爱意"],
+      "english": []
+    },
+    {
+      "word": "愛する",
+      "hiragana": "あいする",
+      "meanings": ["热爱；深爱"],
+      "english": []
+    }
+  ]
+}
+```
 
 - **状态码**:
     - `200`: 查询成功
diff --git a/app/api/search_dict/routes.py b/app/api/search_dict/routes.py
index 83c1dd0..587ff8f 100644
--- a/app/api/search_dict/routes.py
+++ b/app/api/search_dict/routes.py
@@ -6,11 +6,10 @@ from fastapi import APIRouter, Depends, HTTPException, Request, Form
 from app.api.search_dict import service
 from app.api.search_dict.search_schemas import SearchRequest, WordSearchResponse, SearchItemFr, SearchItemJp, \
     ProverbSearchRequest
-from app.api.search_dict.service import suggest_autocomplete
 from app.api.word_comment.word_comment_schemas import CommentSet
-from app.models import DefinitionJp, CommentFr, CommentJp
+from app.models import DefinitionJp, CommentFr, CommentJp, WordlistFr
 from app.models.fr import DefinitionFr, ProverbFr
-from app.models.jp import IdiomJp
+from app.models.jp import IdiomJp, WordlistJp
 from app.utils.all_kana import all_in_kana
 from app.utils.security import get_current_user
 from app.utils.textnorm import normalize_text
@@ -158,8 +157,61 @@ async def search_word_list(query_word: SearchRequest, user=Depends(get_current_u
     :return: 待选列表
     """
     # print(query_word.query, query_word.language, query_word.sort, query_word.order)
-    word_contents = await suggest_autocomplete(query=query_word)
-    return {"list": word_contents}
+    query = query_word.query
+    lang = query_word.language
+    query, search_lang, transable = await service.detect_language(text=query)
+    word_contents = []
+    if lang == "fr":
+        if search_lang == "fr":
+            word_contents = await service.suggest_autocomplete(
+                query=query,
+                dict_lang="fr",
+                model=WordlistFr,
+            )
+            if not transable:
+                word_contents.extend(
+                    await service.search_definition_by_meaning(
+                        query=query,
+                        model=DefinitionFr,
+                        lang="en",
+                    )
+                )
+        else:
+            word_contents = await service.search_definition_by_meaning(
+                query=query_word.query,
+                model=DefinitionFr,
+                lang="zh",
+            )
+    else:
+        if search_lang == "jp":
+            word_contents = await service.suggest_autocomplete(
+                query=query,
+                dict_lang="jp",
+                model=WordlistJp,
+            )
+        elif search_lang == "zh":
+            word_contents = []
+            if transable:
+                word_contents = await service.suggest_autocomplete(
+                    query=query,
+                    dict_lang="jp",
+                    model=WordlistJp,
+                )
+            word_contents.extend(
+                await service.search_definition_by_meaning(
+                    query=query_word.query,
+                    model=DefinitionJp,
+                    lang="zh",
+                )
+            )
+        else:
+            word_contents = await service.suggest_autocomplete(
+                query=query,
+                dict_lang="jp",
+                model=WordlistJp,
+            )
+    suggest_list = service.merge_word_results(word_contents)
+    return {"list": suggest_list}
 
 
 @dict_search.post("/search/list/proverb")
@@ -177,7 +229,8 @@ async def search_proverb_list(query_word: ProverbSearchRequest, user=Depends(get
 
 @dict_search.post("/search/proverb")
 async def search_proverb(proverb_id: int = Form(...), user=Depends(get_current_user)):
-    result = await service.accurate_idiom_proverb(search_id=proverb_id, model=ProverbFr, only_fields=["text", "chi_exp"])
+    result = await service.accurate_idiom_proverb(search_id=proverb_id, model=ProverbFr,
+                                                  only_fields=["text", "chi_exp"])
 
     return {"result": result}
 
@@ -225,5 +278,6 @@ async def search_idiom_list(query_idiom: ProverbSearchRequest, user=Depends(get_
 
 @dict_search.post("/search/idiom")
 async def search_idiom(query_id: int, user=Depends(get_current_user)):
-    result = await service.accurate_idiom_proverb(search_id=query_id, model=IdiomJp, only_fields=["id", "text", "search_text", "chi_exp", "example"])
+    result = await service.accurate_idiom_proverb(search_id=query_id, model=IdiomJp,
+                                                  only_fields=["id", "text", "search_text", "chi_exp", "example"])
     return {"result": result}
diff --git a/app/api/search_dict/service.py b/app/api/search_dict/service.py
index 6cd7f9e..08a8c3e 100644
--- a/app/api/search_dict/service.py
+++ b/app/api/search_dict/service.py
@@ -1,15 +1,13 @@
 import re
-from typing import List, Tuple, Dict, Literal, Type
+from typing import List, Tuple, Dict, Literal, Type, Any
 
 from fastapi import HTTPException
 from redis.asyncio import Redis
 from tortoise import Tortoise, Model
 from tortoise.expressions import Q
 
-from app.api.search_dict.search_schemas import SearchRequest, ProverbSearchRequest
-from app.models import WordlistFr, WordlistJp, KangjiMapping
+from app.models import KangjiMapping
 from app.utils.all_kana import all_in_kana
-from app.utils.textnorm import normalize_text
 from settings import TORTOISE_ORM
 
 
@@ -57,8 +55,12 @@ async def detect_language(text: str) -> Tuple[str, str, bool]:
         return text, "zh", False
 
     # ✅ Step 3: 拉丁字母检测（如法语）
-    if re.search(r"[a-zA-ZÀ-ÿ]", text):
-        return text, "fr", False
+    if re.search(r"[À-ÿ]", text):
+        return text, "fr", True  # True → 含拉丁扩展（非英语）
+
+    # 全部为纯英文字符
+    elif re.fullmatch(r"[a-zA-Z]+", text):
+        return text, "fr", False  # False → 英语单词
 
     # ✅ Step 4: 其他情况（符号、空格等）
     return text, "other", False
@@ -75,171 +77,230 @@ async def accurate_idiom_proverb(search_id: int, model: Type[Model], only_fields
     return result
 
 
-async def suggest_proverb(
+async def suggest_autocomplete(
         query: str,
-        lang: Literal["fr", "zh", "jp"],
+        dict_lang: Literal["fr", "jp"],
         model: Type[Model],
         search_field: str = "search_text",
-        target_field: str = "text",
-        chi_exp_field: str = "chi_exp",
+        text_field: str = "text",
+        hira_field: str = "hiragana",
+        freq_field: str = "freq",
+        english_field: str = "eng_explanation",
         limit: int = 10,
 ) -> List[Dict[str, str]]:
+    """
+    通用自动补全建议接口（增强版）：
+    - 法语: 按 search_text / text 搜索，同时反查 DefinitionFr 的英文释义
+    - 日语: 先按原文 text 匹配，再按假名 search_text 匹配
+    统一返回结构：
+    [
+        {
+            "word": "étudier",
+            "hiragana": None,
+            "meanings": [],
+            "english": ["to study", "to learn"]
+        }
+    ]
+    """
     keyword = query.strip()
     if not keyword:
         return []
 
-    # ✅ 搜索条件：中文时双字段联合匹配
-    if lang == "zh":
-        start_condition = Q(**{f"{chi_exp_field}__istartswith": keyword}) | Q(
-            **{f"{search_field}__istartswith": keyword})
-        contain_condition = Q(**{f"{chi_exp_field}__icontains": keyword}) | Q(**{f"{search_field}__icontains": keyword})
+    # ========== 法语 ==========
+    if dict_lang == "fr":
+        start_condition = Q(**{f"{search_field}__istartswith": keyword}) | Q(**{f"{text_field}__istartswith": keyword})
+        contain_condition = Q(**{f"{search_field}__icontains": keyword}) | Q(**{f"{text_field}__icontains": keyword})
+        value_fields = ["id", text_field, freq_field, search_field]
+
+    # ========== 日语 ==========
+    elif dict_lang == "jp":
+        kana_word = all_in_kana(keyword)
+        start_condition = Q(**{f"{text_field}__istartswith": keyword})
+        contain_condition = Q(**{f"{text_field}__icontains": keyword})
+
+        kana_start = Q(**{f"{hira_field}__istartswith": kana_word})
+        kana_contain = Q(**{f"{hira_field}__icontains": kana_word})
+
+        start_condition |= kana_start
+        contain_condition |= kana_contain
+        value_fields = ["id", text_field, hira_field, freq_field]
+
     else:
-        start_condition = Q(**{f"{search_field}__istartswith": keyword})
-        contain_condition = Q(**{f"{search_field}__icontains": keyword})
+        return []
 
     # ✅ 1. 开头匹配
     start_matches = await (
         model.filter(start_condition)
-        .order_by("-freq", "id")
+        .order_by(f"-{freq_field}", "id")
         .limit(limit)
-        .values("id", target_field, chi_exp_field, "search_text")
+        .values(*value_fields)
     )
 
-    # ✅ 2. 包含匹配（但不是开头）
+    # ✅ 2. 包含匹配
     contain_matches = await (
         model.filter(contain_condition & ~start_condition)
-        .order_by("-freq", "id")
+        .order_by(f"-{freq_field}", "id")
         .limit(limit)
-        .values("id", target_field, chi_exp_field, "search_text")
+        .values(*value_fields)
     )
 
-    # ✅ 3. 合并去重保持顺序
+    # ✅ 3. 合并去重
     results = []
     seen_ids = set()
     for row in start_matches + contain_matches:
-        if row["id"] not in seen_ids:
-            seen_ids.add(row["id"])
-            results.append({
-                "id": row["id"],
-                "proverb": row[target_field],
-                "search_text": row["search_text"],
-                "chi_exp": row[chi_exp_field],
-            })
+        if row["id"] in seen_ids:
+            continue
+        seen_ids.add(row["id"])
+
+        result = {
+            "word": row[text_field],
+            "hiragana": row.get(hira_field) if dict_lang == "jp" else None,
+            "meanings": [],
+            "english": [],
+        }
+
+        # ✅ 若为法语，则反查 DefinitionFr 的英文释义
+        if dict_lang == "fr":
+            # 获取关联的 definitions
+            word_obj = await model.get(id=row["id"]).prefetch_related("definitions")
+            english_list = [
+                d.eng_explanation.strip()
+                for d in word_obj.definitions
+                if d.eng_explanation and d.eng_explanation.strip()
+            ]
+            result["english"] = list(set(english_list))
+
+        results.append(result)
 
-    # ✅ 截断最终返回数量
     return results[:limit]
 
 
-async def suggest_autocomplete(query: SearchRequest, limit: int = 10):
+# ===================================================
+# ✅ 释义反查接口（返回统一结构）
+# ===================================================
+
+async def search_definition_by_meaning(
+        query: str,
+        model: Type[Model],
+        meaning_field: str = "meaning",
+        eng_field: str = "eng_explanation",
+        hira_field: str = "hiragana",
+        limit: int = 20,
+        lang: Literal["zh", "en"] = "zh",
+) -> List[Dict[str, str]]:
+    """
+    双语释义反查接口（中文/英文）：
+    统一返回结构：
+    [
+        {
+            "word": "étudier",
+            "hiragana": None,
+            "meanings": ["学习", "研究"],
+            "english": ["to study"]
+        }
+    ]
     """
 
-    :param query: 当前用户输入的内容
-    :param limit: 返回列表限制长度
-    :return: 联想的单词列表（非完整信息，单纯单词）
-    """
-    if query.language == 'fr':
-        query_word = normalize_text(query.query)
-        exact = await (
-            WordlistFr
-            .get_or_none(search_text=query.query)
-            .values("text", "freq")
-        )
-        if exact:
-            exact_word = [(exact.get("text"), exact.get("freq"))]
-        else:
-            exact_word = []
-
-        qs_prefix = (
-            WordlistFr
-            .filter(Q(search_text__startswith=query_word) | Q(text__startswith=query.query))
-            .exclude(search_text=query.query)
-            .only("text", "freq")
-        )
-        prefix_objs = await qs_prefix[:limit]
-        prefix: List[Tuple[str, int]] = [(o.text, o.freq) for o in prefix_objs]
-
-        need = max(0, limit - len(prefix))
-        contains: List[Tuple[str, int]] = []
-
-        if need > 0:
-            qs_contain = (
-                WordlistFr
-                .filter(Q(search_text__icontains=query_word) | Q(text__icontains=query.query))
-                .exclude(Q(search_text__startswith=query_word) | Q(text__startswith=query.query) | Q(text=query.query))
-                .only("text", "freq")
-                .only("text", "freq")
-            )
-            contains_objs = await qs_contain[: need * 2]
-            contains = [(o.text, o.freq) for o in contains_objs]
-
-            seen_text, out = set(), []
-            for text, freq in list(exact_word) + list(prefix) + list(contains):
-                key = text
-                if key not in seen_text:
-                    seen_text.add(key)
-                    out.append((text, freq))
-                if len(out) >= limit:
-                    break
-            out = sorted(out, key=lambda w: (-w[2], len(w[0]), w[0]))
-            return [text for text, _ in out]
+    keyword = query.strip()
+    if not keyword:
+        return []
 
+    if lang == "zh":
+        search_field = meaning_field
+    elif lang == "en":
+        search_field = eng_field
     else:
-        query_word = all_in_kana(query.query)
-        exact = await (
-            WordlistJp
-            .get_or_none(
-                text=query.query
-            )
-            .only("text", "hiragana", "freq")
-        )
-        if exact:
-            exact_word = [(exact.text, exact.hiragana, exact.freq)]
-        else:
-            exact_word = []
+        raise ValueError("lang 参数必须为 'zh' 或 'en'")
 
-        qs_prefix = (
-            WordlistJp
-            .filter(Q(hiragana__startswith=query_word) | Q(text__startswith=query.query))
-            .exclude(text=query.query)
-            .only("text", "hiragana", "freq")
-        )
-        prefix_objs = await qs_prefix[:limit]
-        prefix: List[Tuple[str, str, int]] = [(o.text, o.hiragana, o.freq) for o in prefix_objs]
+    contain_condition = Q(**{f"{search_field}__icontains": keyword})
 
-        need = max(0, limit - len(prefix))
-        contains: List[Tuple[str, str, int]] = []
-
-        if need > 0:
-            qs_contain = await (
-                WordlistJp
-                .filter(Q(hiragana__icontains=query_word) | Q(text__icontains=query.query))
-                .exclude(Q(hiragana__startswith=query_word) | Q(text__startswith=query.query) | Q(text=query.query))
-                .only("text", "hiragana", "freq")
-            )
-            contains_objs = qs_contain[:need * 2]
-            contains: List[Tuple[str, str, int]] = [(o.text, o.hiragana, o.freq) for o in contains_objs]
-
-        seen_text, out = set(), []
-        for text, hiragana, freq in list(exact_word) + list(prefix) + list(contains):
-            key = (text, hiragana)
-            if key not in seen_text:
-                seen_text.add(key)
-                out.append((text, hiragana, freq))
-            if len(out) >= limit:
-                break
-        out = sorted(out, key=lambda w: (-w[2], len(w[0]), w[0]))
-        return [(text, hiragana) for text, hiragana, _ in out]
-
-
-async def __test():
-    query_word: str = '棋逢'
-    return await (
-        suggest_proverb(
-            query=ProverbSearchRequest(query=query_word),
-            lang='zh'
-        )
+    matches = (
+        await model.filter(contain_condition)
+        .prefetch_related("word")
+        .order_by("id")
     )
 
+    word_to_data: Dict[str, Dict[str, List[str] | str | None]] = {}
+
+    for entry in matches:
+        word_obj = await entry.word
+        word_text = getattr(word_obj, "text", None)
+        if not word_text:
+            continue
+
+        chi_mean = getattr(entry, meaning_field, "").strip() or None
+        eng_mean = getattr(entry, eng_field, "").strip() or None
+        hira_text = getattr(word_obj, hira_field, None) if hasattr(word_obj, hira_field) else None
+
+        if word_text not in word_to_data:
+            word_to_data[word_text] = {"hiragana": hira_text, "meanings": [], "english": []}
+
+        if chi_mean:
+            word_to_data[word_text]["meanings"].append(chi_mean)
+        if eng_mean:
+            word_to_data[word_text]["english"].append(eng_mean)
+
+    results = []
+    for word, data in word_to_data.items():
+        results.append({
+            "word": word,
+            "hiragana": data["hiragana"],
+            "meanings": list(set(data["meanings"])),
+            "english": list(set(data["english"]))
+        })
+
+    return results[:limit]
+
+
+def merge_word_results(*lists: List[Dict[str, Any]]) -> List[Dict[str, object]]:
+    """
+    合并多个结果列表并去重：
+    - 依据 word（+ hiragana）唯一性去重
+    - meanings / english 合并去重
+    - 保留最早出现的顺序
+    """
+    merged: Dict[str, Dict[str, Any]] = {}
+    order: List[str] = []
+
+    for lst in lists:
+        for item in lst:
+            word = item.get("word")
+            hira = item.get("hiragana")
+            key = f"{word}:{hira or ''}"  # 以 word+hiragana 作为唯一标识
+
+            if key not in merged:
+                # 初次出现，加入结果集
+                merged[key] = {
+                    "word": word,
+                    "hiragana": hira,
+                    "meanings": list(item.get("meanings", [])),
+                    "english": list(item.get("english", []))
+                }
+                order.append(key)
+            else:
+                # 已存在 → 合并释义和英文解释
+                merged[key]["meanings"] = list(set(
+                    list(merged[key].get("meanings", [])) +
+                    list(item.get("meanings", []) or [])
+                ))
+                merged[key]["english"] = list(set(
+                    list(merged[key].get("english", [])) +
+                    list(item.get("english", []) or [])
+                ))
+
+    # 保持插入顺序输出
+    return [merged[k] for k in order]
+
+
+# async def __test():
+#     query_word: str = '棋逢'
+#     return await (
+#         suggest_proverb(
+#             query=ProverbSearchRequest(query=query_word),
+#             lang='zh'
+#         )
+#     )
+
 
 async def __main():
     await Tortoise.init(config=TORTOISE_ORM)