diff --git a/src/main/java/org/dromara/easyai/unet/UNetEncoder.java b/src/main/java/org/dromara/easyai/unet/UNetEncoder.java
new file mode 100644
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+++ b/src/main/java/org/dromara/easyai/unet/UNetEncoder.java
@@ -0,0 +1,181 @@
+package org.dromara.easyai.unet;
+
+import org.dromara.easyai.conv.ConvCount;
+import org.dromara.easyai.entity.ThreeChannelMatrix;
+import org.dromara.easyai.i.ActiveFunction;
+import org.dromara.easyai.i.OutBack;
+import org.dromara.easyai.matrixTools.Matrix;
+import org.dromara.easyai.matrixTools.MatrixOperation;
+import org.dromara.easyai.nerveEntity.ConvParameter;
+import org.dromara.easyai.nerveEntity.ConvSize;
+
+import java.util.*;
+
+/**
+ * @author lidapeng
+ * @time 2025/3/2 07:51
+ * @des unet编码器
+ */
+public class UNetEncoder extends ConvCount {
+    private final ConvParameter convParameter = new ConvParameter();//内存中卷积层模型及临时数据
+    private final MatrixOperation matrixOperation = new MatrixOperation();
+    private final int kerSize;
+    private final float studyRate;//学习率
+    private final int deep;//当前深度
+    private final int channelNo;//卷积层数
+    private List<Matrix> decodeErrorMatrix;//从解码器传来的误差矩阵
+    private final ActiveFunction activeFunction;
+    private UNetEncoder afterEncoder;//下一个编码器
+    private UNetEncoder beforeEncoder;//上一个编码器
+    private UNetDecoder decoder;//下一个解码器
+    private final int xSize;
+    private final int ySize;
+    private final float oneStudyRate;
+    private final float gaMa;
+    private final float gMaxTh;
+    private final boolean aoTu;
+
+    public UNetEncoder(int kerSize, int channelNo, int deep, ActiveFunction activeFunction
+            , float studyRate, int xSize, int ySize, float oneStudyRate, float gaMa, float gMaxTh, boolean aoTu) throws Exception {//核心大小
+        Random random = new Random();
+        this.xSize = xSize;
+        this.aoTu = aoTu;
+        this.gMaxTh = gMaxTh;
+        this.gaMa = gaMa;
+        this.ySize = ySize;
+        this.oneStudyRate = oneStudyRate;
+        this.studyRate = studyRate;
+        this.kerSize = kerSize;
+        this.activeFunction = activeFunction;
+        this.deep = deep;
+        this.channelNo = channelNo;
+        List<Matrix> nerveMatrixList = convParameter.getNerveMatrixList();
+        List<ConvSize> convSizeList = convParameter.getConvSizeList();
+        List<Matrix> dymStudyRateList = convParameter.getDymStudyRateList();
+        for (int i = 0; i < channelNo; i++) {
+            initNervePowerMatrix(random, nerveMatrixList, dymStudyRateList);
+            convSizeList.add(new ConvSize());
+        }
+        if (deep == 1) {
+            List<List<Float>> oneConvPowers = new ArrayList<>();
+            List<List<Float>> oneDymStudyRateList = new ArrayList<>();
+            for (int k = 0; k < channelNo; k++) {
+                List<Float> oneConvPower = new ArrayList<>();
+                List<Float> oneDymStudyRate = new ArrayList<>();
+                oneConvPowers.add(oneConvPower);
+                oneDymStudyRateList.add(oneDymStudyRate);
+                //通道数
+                int channelNum = 3;
+                for (int i = 0; i < channelNum; i++) {
+                    oneConvPower.add(random.nextFloat() / channelNum);
+                    oneDymStudyRate.add(0f);
+                }
+            }
+            convParameter.setOneDymStudyRateList(oneDymStudyRateList);
+            convParameter.setOneConvPower(oneConvPowers);
+        }
+    }
+
+    public ConvParameter getConvParameter() {
+        return convParameter;
+    }
+
+    protected void setDecodeErrorMatrix(List<Matrix> decodeErrorMatrix) {
+        this.decodeErrorMatrix = decodeErrorMatrix;
+    }
+
+    protected List<Matrix> getAfterConvMatrix(long eventID) {//卷积后的矩阵
+        List<Matrix> outMatrixList = convParameter.getFeatureMap().get(eventID);
+        convParameter.getFeatureMap().remove(eventID);
+        return outMatrixList;
+    }
+
+    //发送特征三通道矩阵
+    public void sendThreeChannel(long eventID, OutBack outBack, ThreeChannelMatrix feature, ThreeChannelMatrix featureE,
+                                 boolean study) throws Exception {
+        if (study && featureE == null) {
+            throw new Exception("训练时期望矩阵不能为空");
+        }
+        if (feature.getX() != xSize && feature.getY() != ySize) {
+            throw new Exception("输入图片尺寸与初始化参数不一致");
+        }
+        List<Matrix> matrixList = new ArrayList<>();
+        matrixList.add(feature.getMatrixR());
+        matrixList.add(feature.getMatrixG());
+        matrixList.add(feature.getMatrixB());
+        if (study) {
+            convParameter.setFeatureMatrixList(matrixList);
+        }
+        sendMatrixList(eventID, outBack, featureE, matrixList, study, feature);
+    }
+
+    protected void sendFeature(long eventID, OutBack outBack, ThreeChannelMatrix featureE,
+                               List<Matrix> myFeatures, boolean study, ThreeChannelMatrix backGround) throws Exception {
+        List<Matrix> convMatrixList = downConvAndPooling(myFeatures, convParameter, channelNo, activeFunction, kerSize, true, eventID);
+        if (afterEncoder != null) {//后面还有编码器，继续向后传递
+            afterEncoder.sendFeature(eventID, outBack, featureE, convMatrixList, study, backGround);
+        } else {//向解码器传递
+            decoder.sendFeature(eventID, outBack, featureE, convMatrixList, study, backGround);
+        }
+    }
+
+    protected void backError(List<Matrix> errorMatrix) throws Exception {//接收误差
+        List<Matrix> errorList = backDownPoolingByList(errorMatrix, convParameter.getOutX(), convParameter.getOutY());//池化误差返回
+        List<Matrix> errorMatrixList = matrixOperation.addMatrixList(errorList, decodeErrorMatrix);
+        List<Matrix> myErrorMatrix = backAllDownConv(convParameter, errorMatrixList, studyRate, activeFunction, channelNo, kerSize,
+                gaMa, gMaxTh, aoTu);
+        if (beforeEncoder != null) {
+            beforeEncoder.backError(myErrorMatrix);
+        } else {//最后一层 调整1v1卷积
+            backOneConvByList(myErrorMatrix, convParameter.getFeatureMatrixList(), convParameter.getOneConvPower(), oneStudyRate
+                    , convParameter.getOneDymStudyRateList(), gaMa, gMaxTh, aoTu);
+        }
+    }
+
+    public void sendMatrixList(long eventID, OutBack outBack, ThreeChannelMatrix featureE, List<Matrix> feature,
+                               boolean study, ThreeChannelMatrix backGround) throws Exception {
+        List<Matrix> myFeatures = manyOneConv(feature, convParameter.getOneConvPower());//矩阵重新调整维度
+        List<Matrix> convMatrixList = downConvAndPooling(myFeatures, convParameter, channelNo, activeFunction, kerSize, true, eventID);
+        if (afterEncoder != null) {//后面还有编码器，继续向后传递
+            afterEncoder.sendFeature(eventID, outBack, featureE, convMatrixList, study, backGround);
+        } else {//向解码器传递
+            decoder.sendFeature(eventID, outBack, featureE, convMatrixList, study, backGround);
+        }
+    }
+
+    private void initNervePowerMatrix(Random random, List<Matrix> nervePowerMatrixList, List<Matrix> dymStudyRageList) throws Exception {
+        int convSize = kerSize * kerSize;
+        Matrix nervePowerMatrix = new Matrix(convSize, 1);
+        for (int i = 0; i < convSize; i++) {
+            float power = random.nextFloat() / kerSize;
+            nervePowerMatrix.setNub(i, 0, power);
+        }
+        dymStudyRageList.add(new Matrix(convSize, 1));
+        nervePowerMatrixList.add(nervePowerMatrix);
+    }
+
+    public UNetEncoder getAfterEncoder() {
+        return afterEncoder;
+    }
+
+    public void setAfterEncoder(UNetEncoder afterEncoder) {
+        this.afterEncoder = afterEncoder;
+    }
+
+    public UNetEncoder getBeforeEncoder() {
+        return beforeEncoder;
+    }
+
+    public void setBeforeEncoder(UNetEncoder beforeEncoder) {
+        this.beforeEncoder = beforeEncoder;
+    }
+
+    public UNetDecoder getDecoder() {
+        return decoder;
+    }
+
+    public void setDecoder(UNetDecoder decoder) {
+        this.decoder = decoder;
+    }
+
+}