diff --git a/classifier/embedding_food_classifier_app.py b/classifier/embedding_food_classifier_app.py
index cce0efb..d386498 100644
--- a/classifier/embedding_food_classifier_app.py
+++ b/classifier/embedding_food_classifier_app.py
@@ -1100,6 +1100,8 @@ class EmbeddingFoodClassifierApp:
             
             # 在FAISS索引中搜索最相似的k张图片
             scores, indices = self.faiss_index.search(query_embedding, k)
+            print("最相似的图片索引:", indices)
+            print("最相似的图片分数:", scores)
             
             # 收集相似图片的类别
             similar_classes = []
diff --git a/toAndroid/toAndroidSearch.py b/toAndroid/toAndroidSearch.py
new file mode 100644
index 0000000..204157b
--- /dev/null
+++ b/toAndroid/toAndroidSearch.py
@@ -0,0 +1,223 @@
+import os
+import sys
+import json
+import math
+from typing import List, Tuple, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+# 添加项目根目录到路径（与 toAndroidEmbedding.py 保持一致）
+# current_dir = os.path.dirname(os.path.abspath(__file__))
+# parent_dir = os.path.dirname(current_dir)
+# sys.path.insert(0, parent_dir)
+print(sys.path)
+
+
+def _load_embeddings_json(json_path: str) -> torch.Tensor:
+    """
+    读取 ../faiss_vector_db/embeddings.json，尽量兼容常见格式，并返回 [N, 512] 的 float32 Tensor。
+    兼容格式示例：
+      1) [ [float]*512, [float]*512, ... ]
+      2) [ {"embedding": [float]*512, ...}, {"embedding": [...]}, ... ]
+      3) {"embeddings": [ [float]*512, ... ] }
+      4) {"items": [ {"embedding": [...]}, ... ] }
+
+    如维度不为512或为空，抛出异常。
+    """
+    if not os.path.exists(json_path):
+        raise FileNotFoundError(f"未找到 embeddings.json: {json_path}")
+
+    with open(json_path, "r", encoding="utf-8") as f:
+        data = json.load(f)
+
+    def extract_vectors(obj: Union[list, dict]) -> List[List[float]]:
+        if isinstance(obj, list):
+            # list of vectors or list of dicts
+            if len(obj) == 0:
+                return []
+            if isinstance(obj[0], list):
+                return obj
+            if isinstance(obj[0], dict):
+                vecs = []
+                for item in obj:
+                    if "embedding" in item and isinstance(item["embedding"], list):
+                        vecs.append(item["embedding"])
+                return vecs
+            return []
+        elif isinstance(obj, dict):
+            # possible keys: "embeddings", "items", "data"
+            for key in ("embeddings", "items", "data", "vectors"):
+                if key in obj:
+                    inner = obj[key]
+                    return extract_vectors(inner)
+            # maybe dict of id->vector
+            # ensure values are lists
+            values = list(obj.values())
+            if values and isinstance(values[0], list):
+                return values
+            if values and isinstance(values[0], dict) and "embedding" in values[0]:
+                return [v["embedding"] for v in values if "embedding" in v]
+            return []
+        else:
+            return []
+
+    vectors = extract_vectors(data)
+    if not vectors:
+        raise ValueError("embeddings.json 未解析到任何向量，请确认文件结构。")
+
+    # 验证维度
+    dim = len(vectors[0])
+    if dim != 512:
+        raise ValueError(f"向量维度为 {dim}，与期望的512不符。请检查 embeddings.json。")
+
+    # 转为 Tensor
+    emb_tensor = torch.tensor(vectors, dtype=torch.float32)  # [N, 512]
+    if emb_tensor.ndim != 2 or emb_tensor.shape[1] != 512:
+        raise ValueError("向量张量形状异常，期望 [N, 512]。")
+
+    return emb_tensor
+
+
+class TwoLayerSimilarityNet(nn.Module):
+    """
+    两层网络：
+      - layer1: Linear(512->512) 无偏置，初始化为恒等（可视作占位层，满足“两层”要求）
+      - layer2: Linear(512->N) 无偏置，权重为数据库向量（每行一个向量），用于点积相似度
+
+    为近似余弦相似度：
+      - 构造时将数据库向量 L2 归一化后作为 layer2.weight
+      - 前向时对输入向量做 L2 归一化，再依次通过 layer1、layer2
+    """
+
+    def __init__(self, db_vectors: torch.Tensor):
+        """
+        db_vectors: [N, 512] float32
+        """
+        super().__init__()
+
+        if db_vectors.ndim != 2 or db_vectors.shape[1] != 512:
+            raise ValueError("db_vectors 形状必须为 [N, 512]")
+
+        N = db_vectors.shape[0]
+
+        # （已移除恒等层，直接使用单层相似度投影）
+
+        # 单层：权重为数据库向量（行表示样本），无偏置
+        self.layer2 = nn.Linear(512, N, bias=False)
+
+        # 归一化数据库向量 -> 近似余弦相似度
+        # db_norm = F.normalize(db_vectors, p=2, dim=1)  # [N, 512], 每行单位范数
+        with torch.no_grad():
+            # nn.Linear 的 weight 形状为 [out_features, in_features] = [N, 512]
+            # self.layer2.weight.copy_(db_norm)
+            self.layer2.weight.copy_(db_vectors)
+
+        # 冻结所有参数（移动端推理仅用作检索头）
+        for p in self.parameters():
+            p.requires_grad = False
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """
+        x: [B, 512] float32
+        返回: [B, N] 相似度（点积，因归一化近似余弦）
+        """
+        if x.ndim != 2 or x.shape[1] != 512:
+            raise ValueError("输入张量形状必须为 [batch, 512]")
+
+        # 输入归一化
+        # x_norm = F.normalize(x, p=2, dim=1)  # [B, 512]
+
+        # 单层线性变换
+        # sims = self.layer2(x_norm)          # [B, N]
+        sims = self.layer2(x)          # [B, N]
+
+        return sims
+
+
+def build_and_export_similarity_head(
+    embeddings_json_path: str = "faiss_vector_db/embeddings.json",
+    output_path: str = "faiss_vector_db/similarity_head.pt",
+) -> Tuple[str, int]:
+    """
+    构建两层相似度网络并导出 TorchScript。
+    返回 (输出路径, 样本数)
+    """
+    print("开始构建相似度检索头（TwoLayerSimilarityNet）...")
+
+    # 1) 加载原始向量
+    db_vectors = _load_embeddings_json(embeddings_json_path)  # [N, 512]
+    N = db_vectors.shape[0]
+    print(f"✓ 已加载数据库向量，样本数: {N}，维度: 512")
+
+    # 2) 构建网络
+    model = TwoLayerSimilarityNet(db_vectors)
+    model.eval()
+    print("✓ 单层检索头构建完成（layer2=向量库权重，无偏置）")
+
+    # 3) 进行一次简单测试
+    with torch.no_grad():
+        test_input = torch.rand(2, 512, dtype=torch.float32)
+        test_output = model(test_input)
+        print(f"✓ 前向测试成功，输入形状: {tuple(test_input.shape)}，输出形状: {tuple(test_output.shape)}")
+        print(f"  输出值范围: [{test_output.min().item():.4f}, {test_output.max().item():.4f}]")
+
+    # 4) 导出为 TorchScript（使用 trace，输入为 [1, 512]）
+    try:
+        example = torch.rand(1, 512, dtype=torch.float32)
+        traced = torch.jit.trace(model, example)
+        traced.save(output_path)
+        print(f"✓ TorchScript 模型已保存到: {output_path}")
+
+        # 验证加载
+        loaded = torch.jit.load(output_path)
+        with torch.no_grad():
+            out = loaded(example)
+            if out.shape != (1, N):
+                raise RuntimeError("加载后的 TorchScript 输出形状异常")
+        print("✓ 导出模型验证通过")
+    except Exception as e:
+        print(f"✗ TorchScript 导出失败: {e}")
+        raise
+
+    # 5) 信息统计
+    model_size_mb = os.path.getsize(output_path) / (1024 * 1024.0)
+    total_params = sum(p.numel() for p in model.parameters())
+    print("\n" + "=" * 60)
+    print("检索头模型信息:")
+    print("=" * 60)
+    print(f"样本数 (输出维度): {N}")
+    print(f"模型文件大小: {model_size_mb:.2f} MB")
+    print(f"参数总数: {total_params:,} (全部冻结)")
+    print("=" * 60)
+
+    return output_path, N
+
+
+def main():
+    print("开始转换检索头为移动端格式（TorchScript）...")
+    embeddings_path = "../faiss_vector_db/faiss_index/embeddings.json"
+    output_path = "../faiss_vector_db/faiss_index/similarity_head.pt"
+
+    try:
+        path, n_items = build_and_export_similarity_head(embeddings_path, output_path)
+        print("\nAndroid端使用说明：")
+        print("1) 输入为 [batch, 512] 的 float32 张量（由移动端Embedding模型输出）")
+        print("2) 输出为 [batch, N] 的相似度，其中 N 为向量库样本数")
+        print("3) 打包到APK后，使用 PyTorch Mobile 加载 similarity_head.pt 进行推理")
+        print("4) 如需做 Top-K 检索，取每行的最大/Top-K即可")
+        print("\n示例（Kotlin/Java 伪代码）：")
+        print("  val module = LiteModuleLoader.load(assetFilePath(\"similarity_head.pt\"))")
+        print("  val inputTensor = Tensor.fromBlob(inputArray, longArrayOf(batch.toLong(), 512L))")
+        print("  val outputTensor = module.forward(IValue.from(inputTensor)).toTensor()")
+        print("  val sims = outputTensor.dataAsFloatArray  // 长度 = batch * N")
+        print("\n完成。")
+    except Exception as e:
+        print(f"✗ 转换失败: {e}")
+        import traceback
+        traceback.print_exc()
+
+
+
+main()
\ No newline at end of file