diff --git a/app/api/v1/meituan.py b/app/api/v1/meituan.py
index 7cb1c4d..3cb508c 100644
--- a/app/api/v1/meituan.py
+++ b/app/api/v1/meituan.py
@@ -8,12 +8,12 @@ import logging
 from concurrent.futures import ThreadPoolExecutor, as_completed
 
 from fastapi import APIRouter, Depends, HTTPException
-from sqlalchemy import select
+from sqlalchemy import nullslast, select
 from sqlalchemy.orm import Session, aliased
 
 from app.core.config import settings
 from app.db.session import get_db
-from app.integrations.meituan import MeituanCpsError, get_referral_link, query_coupon
+from app.integrations.meituan import MeituanCpsError, _call, get_referral_link, query_coupon
 from app.models.meituan_coupon import MeituanCoupon
 from app.schemas.meituan import (
     CouponCard,
@@ -93,7 +93,7 @@ def _commission_pct(card: CouponCard) -> float:
 
 
 @router.post("/feed", response_model=FeedResponse, summary="混合feed(外卖+到店交叉);tab=rec智能推荐/distance距离最近")
-def feed(req: FeedRequest) -> FeedResponse:
+def feed(req: FeedRequest, db: Session = Depends(get_db)) -> FeedResponse:
     if not settings.mt_cps_configured:
         return FeedResponse(items=[], has_next=False, page=req.page)
     lon, lat = req.longitude, req.latitude
@@ -110,28 +110,87 @@ def feed(req: FeedRequest) -> FeedResponse:
         except MeituanCpsError:
             return []
 
-    # 智能推荐 / 距离最近:拉齐全部榜单轮次(~60 条),后端处理后【一次性返回、不分页】。
-    # (池子很小;逐页实时打美团反而让客户端翻页卡顿/滑不动——一次拉齐 → 前端加载一次、滚动顺畅。)
-    if tab in ("rec", "distance"):
-        wm_all: list[dict] = []
-        dd_all: list[dict] = []
-        with ThreadPoolExecutor(max_workers=len(_TOPIC_ROUNDS) * 2) as pool:
-            futs = [
-                (pool.submit(_fetch_topic, 1, None, wm_topic),
-                 pool.submit(_fetch_topic, 2, 1, dd_topic))
-                for wm_topic, dd_topic in _TOPIC_ROUNDS
-            ]
-            for fw, fd in futs:
-                wm_all += fw.result()
-                dd_all += fd.result()
-        cards = _interleave(wm_all, dd_all)   # from_raw + 去重 + 外卖:到店 2:1 交叉
-        if tab == "rec":
-            # 智能推荐:去掉佣金率 < 3%
-            cards = [c for c in cards if _commission_pct(c) >= 3.0]
-        else:
-            # 距离最近:在完整池上全局按距离由近及远(无距离排最后)
-            cards.sort(key=lambda c: c.distance_meters if c.distance_meters is not None else float("inf"))
-        return FeedResponse(items=cards, has_next=False, page=1)
+    # 距离最近:搜索召回(外卖搜"外卖" + 到店搜"美食",都 sortField=6 离我最近)一页页拉。
+    # 搜索翻页必须用 searchId(pageNo 翻不动),所以每个 feed 页顺序翻到第 N 页;两路并行、page 1 最快。
+    # 无状态、不改 APP(传页码即可);按你位置实时算距离(库里没存 POI 经纬度,只能实时)。
+    if tab == "distance":
+        lon_i, lat_i = int(lon * 1_000_000), int(lat * 1_000_000)
+
+        def _search_page_n(platform: int, biz_line: int | None, keyword: str, n: int) -> tuple[list[dict], bool]:
+            """顺序翻到第 n 页(搜索须 searchId 续页),返回(第 n 页 items, 是否还有下一页)。"""
+            sid: str | None = None
+            data: list[dict] = []
+            has_next = False
+            for pg in range(1, n + 1):
+                body: dict = {
+                    "platform": platform, "searchText": keyword, "sortField": 6,
+                    "longitude": lon_i, "latitude": lat_i, "pageSize": 20,
+                }
+                if biz_line is not None:
+                    body["bizLine"] = biz_line
+                if sid:
+                    body["searchId"] = sid
+                else:
+                    body["pageNo"] = 1
+                try:
+                    r = _call("/cps_open/common/api/v1/query_coupon", body)
+                except MeituanCpsError:
+                    return [], False
+                data = r.get("data") or []
+                sid = r.get("searchId")
+                has_next = bool(r.get("hasNext")) and bool(data)
+                if not data or (not has_next and pg < n):
+                    return [], False   # 没那么多页了
+            return data, has_next
+
+        with ThreadPoolExecutor(max_workers=2) as pool:
+            f_wm = pool.submit(_search_page_n, 1, None, "外卖", req.page)
+            f_dd = pool.submit(_search_page_n, 2, 1, "美食", req.page)
+            (wm_data, wm_hn), (dd_data, dd_hn) = f_wm.result(), f_dd.result()
+
+        seen: set[str] = set()
+        cards: list[CouponCard] = []
+        for it in wm_data + dd_data:
+            card = CouponCard.from_raw(it)
+            if card.product_view_sign and card.product_view_sign not in seen:
+                seen.add(card.product_view_sign)
+                cards.append(card)
+        cards.sort(key=lambda c: c.distance_meters if c.distance_meters is not None else float("inf"))
+        return FeedResponse(items=cards, has_next=wm_hn or dd_hn, page=req.page)
+
+    # 智能推荐(rec):走【离线库】筛佣金率 ≥ 3%,分页返回(SQL 侧去重+排序+分页,秒级、不打美团)。
+    # 实测库里佣金≥3% 去重后仅 ~578 条(几乎全是外卖;到店团购佣金普遍 <3%):实时按"同城热销榜单"
+    # 拉既撞限流、又填不满(该榜单中位佣金 ~0.8%,筛完每页剩 0-1 条),故从库出。佣金阈值逻辑不变。
+    if tab == "rec":
+        PAGE = 20
+        base = select(MeituanCoupon).where(MeituanCoupon.commission_percent >= 3.0)
+        deduped = base.distinct(MeituanCoupon.dedup_key).order_by(
+            MeituanCoupon.dedup_key,
+            MeituanCoupon.commission_percent.desc(),
+        ).subquery()
+        m = aliased(MeituanCoupon, deduped)
+        start = (req.page - 1) * PAGE
+        rows = db.execute(
+            select(m)
+            # 销量高的优先(无销量档排后),同档佣金高优先,id 兜底稳定分页
+            .order_by(nullslast(m.sale_volume_num.desc()), m.commission_percent.desc(), m.id)
+            .offset(start)
+            .limit(PAGE + 1)
+        ).scalars().all()
+        has_next = len(rows) > PAGE
+        cards: list[CouponCard] = []
+        for row in rows[:PAGE]:
+            try:
+                card = CouponCard.from_raw(row.raw or {})
+            except Exception:  # noqa: BLE001
+                continue
+            if card.product_view_sign:
+                # 智能推荐不显示距离:库里的距离是相对城市默认点的(对用户无意义、且误导)。
+                # 置空后前端"距离 店名"那行只剩店名、自动顶到最左(店名移到原距离的位置)。
+                card.distance_text = None
+                card.distance_meters = None
+                cards.append(card)
+        return FeedResponse(items=cards, has_next=has_next, page=req.page)
 
     # 默认(老客户端不传 tab):沿用逐轮分页的混合 feed,不筛。
     page_idx = req.page - 1