diff --git a/app/api/api_recommendation.py b/app/api/api_recommendation.py
index 5f71d38..faeb780 100644
--- a/app/api/api_recommendation.py
+++ b/app/api/api_recommendation.py
@@ -134,21 +134,36 @@ async def get_recommendations(user_id: int, category: str, brand_id: int, brand_
 
         # 合并分数
         if brand_id is not None:
-            if brand_id is not None:
-                brand_idx_feature = matrix_data["brand_index_map"].get(brand_id)
-                if brand_idx_feature is not None and valid_sketch_idxs_feature:
-                    raw_brand_feat_scores = matrix_data["brand_feature_matrix"][
-                        brand_idx_feature, valid_sketch_idxs_feature]
-                    raw_brand_feat_scores = (raw_brand_feat_scores - np.min(raw_brand_feat_scores)) / (
-                            np.max(raw_brand_feat_scores) - np.min(raw_brand_feat_scores) + 1e-8)
-                    processed_brand_feat = raw_brand_feat_scores
-                    final_scores = processed_inter + 0.3 * ((1 - brand_scale) * processed_feat + brand_scale * processed_brand_feat)
-                else:
-                    final_scores = processed_inter + 0.3 * processed_feat
+            brand_idx_feature = matrix_data["brand_index_map"].get(brand_id)
+
+            brand_feat_valid = (
+                    matrix_data["brand_feature_matrix"].size > 0 and  # 矩阵非空
+                    brand_idx_feature is not None and
+                    valid_sketch_idxs_feature  # 有可用索引
+            )
+
+            if brand_feat_valid:
+                raw_brand_feat_scores = matrix_data["brand_feature_matrix"][
+                    brand_idx_feature, valid_sketch_idxs_feature
+                ]
+                raw_brand_feat_scores = (raw_brand_feat_scores - np.min(raw_brand_feat_scores)) / (
+                        np.max(raw_brand_feat_scores) - np.min(raw_brand_feat_scores) + 1e-8
+                )
+                processed_brand_feat = raw_brand_feat_scores
+
+                # 如果 processed_feat 是空的，替换为全 0，避免 shape 不一致
+                if processed_feat.size == 0:
+                    processed_feat = np.zeros_like(processed_brand_feat)
+
+                final_scores = processed_inter + 0.3 * (
+                        (1 - brand_scale) * processed_feat + brand_scale * processed_brand_feat
+                )
             else:
-                final_scores = processed_inter + 0.3 * processed_feat
+                # brand 信息不可用
+                final_scores = processed_inter + 0.3 * processed_feat if processed_feat.size > 0 else processed_inter
         else:
-            final_scores = processed_inter + 0.3 * processed_feat
+            final_scores = processed_inter + 0.3 * processed_feat if processed_feat.size > 0 else processed_inter
+
         valid_sketch_idxs = matrix_data["cached_valid_idxs"][cache_key]
 
         # 概率采样
diff --git a/app/service/recommend/scheduled_task.py b/app/service/recommend/scheduled_task.py
index f6b52ef..6dc67e8 100644
--- a/app/service/recommend/scheduled_task.py
+++ b/app/service/recommend/scheduled_task.py
@@ -527,7 +527,7 @@ def update_heat_matrices():
 
 if __name__ == "__main__":
     try:
-        # update_user_matrices()
+        update_user_matrices()
         update_heat_matrices()
         # scheduler = BlockingScheduler()
         # scheduler.add_job(update_user_matrices, 'cron', hour=12, timezone='Asia/Shanghai')
diff --git a/app/service/recommend/service.py b/app/service/recommend/service.py
index b3545f2..0db64dd 100644
--- a/app/service/recommend/service.py
+++ b/app/service/recommend/service.py
@@ -55,9 +55,20 @@ def load_resources():
 
         matrix_data["feature_matrix"] = np.load(f"{RECOMMEND_PATH_PREFIX}feature_matrix.npy", allow_pickle=True)
 
-        matrix_data["brand_feature_matrix"] = np.load(f"{RECOMMEND_PATH_PREFIX}brand_feature_matrix.npy", allow_pickle=True)
+        brand_feature_path = f"{RECOMMEND_PATH_PREFIX}brand_feature_matrix.npy"
+        if os.path.exists(brand_feature_path):
+            matrix_data["brand_feature_matrix"] = np.load(brand_feature_path, allow_pickle=True)
+        else:
+            logger.warning("brand_feature_matrix 文件不存在，使用空数组")
+            matrix_data["brand_feature_matrix"] = np.array([])
 
-        matrix_data["brand_index_map"] = np.load(f"{RECOMMEND_PATH_PREFIX}brand_index_map.npy",allow_pickle=True).item()
+        # brand_index_map
+        brand_index_path = f"{RECOMMEND_PATH_PREFIX}brand_index_map.npy"
+        if os.path.exists(brand_index_path):
+            matrix_data["brand_index_map"] = np.load(brand_index_path, allow_pickle=True).item()
+        else:
+            logger.warning("brand_index_map 文件不存在，使用空字典")
+            matrix_data["brand_index_map"] = {}
 
         matrix_data["user_index_feature"] = np.load(f"{RECOMMEND_PATH_PREFIX}user_index_feature_matrix.npy", allow_pickle=True).item()