diff --git a/src/main/java/org/dromara/easyai/resnet/ConvLay.java b/src/main/java/org/dromara/easyai/resnet/ConvLay.java
new file mode 100644
index 0000000..916d501
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+++ b/src/main/java/org/dromara/easyai/resnet/ConvLay.java
@@ -0,0 +1,78 @@
+package org.dromara.easyai.resnet;
+
+import org.dromara.easyai.matrixTools.Matrix;
+import org.dromara.easyai.matrixTools.MatrixNorm;
+import org.dromara.easyai.resnet.entity.BackParameter;
+import org.dromara.easyai.resnet.entity.NormModel;
+import org.dromara.easyai.resnet.entity.ResConvModel;
+
+import java.util.ArrayList;
+import java.util.List;
+
+/**
+ * @author lidapeng
+ * @time 2025/4/11 17:18
+ * @des 单个卷积层
+ */
+public class ConvLay {
+    private List<Matrix> convPower;//第一层卷积权重 需要作为模型取出
+    private List<Matrix> dymStudyRateList;//动态学习率
+    private List<MatrixNorm> matrixNormList;//归一化层// 需要作为模型取出
+    private final BackParameter backParameter = new BackParameter();
+
+    public List<Matrix> getDymStudyRateList() {
+        return dymStudyRateList;
+    }
+
+    public void setDymStudyRateList(List<Matrix> dymStudyRateList) {
+        this.dymStudyRateList = dymStudyRateList;
+    }
+
+    public ResConvModel getModel() {
+        ResConvModel resConvModel = new ResConvModel();
+        List<NormModel> normModelList = new ArrayList<>();
+        List<Float[]> convPowerList = new ArrayList<>();
+        resConvModel.setNormModelList(normModelList);
+        resConvModel.setConvPowerModelList(convPowerList);
+        for (MatrixNorm matrixNorm : matrixNormList) {
+            normModelList.add(matrixNorm.getModel());
+        }
+        for (Matrix matrix : convPower) {
+            convPowerList.add(matrix.getMatrixModel());
+        }
+        return resConvModel;
+    }
+
+    public void insertModel(ResConvModel resConvModel) {
+        List<NormModel> normModelList = resConvModel.getNormModelList();
+        List<Float[]> convPowerList = resConvModel.getConvPowerModelList();
+        int normSize = matrixNormList.size();
+        for (int i = 0; i < normSize; i++) {
+            matrixNormList.get(i).insertModel(normModelList.get(i));
+        }
+        int nerveSize = convPower.size();
+        for (int i = 0; i < nerveSize; i++) {
+            convPower.get(i).insertMatrixModel(convPowerList.get(i));
+        }
+    }
+
+    public BackParameter getBackParameter() {
+        return backParameter;
+    }
+
+    public List<Matrix> getConvPower() {
+        return convPower;
+    }
+
+    public void setConvPower(List<Matrix> convPower) {
+        this.convPower = convPower;
+    }
+
+    public List<MatrixNorm> getMatrixNormList() {
+        return matrixNormList;
+    }
+
+    public void setMatrixNormList(List<MatrixNorm> matrixNormList) {
+        this.matrixNormList = matrixNormList;
+    }
+}