diff --git a/src/main/java/org/dromara/easyai/transFormer/FirstDecoderBlock.java b/src/main/java/org/dromara/easyai/transFormer/FirstDecoderBlock.java
new file mode 100644
index 0000000..6a7df60
--- /dev/null
+++ b/src/main/java/org/dromara/easyai/transFormer/FirstDecoderBlock.java
@@ -0,0 +1,62 @@
+package org.dromara.easyai.transFormer;
+
+import org.dromara.easyai.i.OutBack;
+import org.dromara.easyai.matrixTools.Matrix;
+import org.dromara.easyai.transFormer.model.FirstDecoderModel;
+import org.dromara.easyai.transFormer.seflAttention.LayNorm;
+import org.dromara.easyai.transFormer.seflAttention.MultiSelfAttention;
+
+import java.util.List;
+
+
+public class FirstDecoderBlock {//解码器模块
+    private final MultiSelfAttention multiSelfAttention;
+    private final LayNorm attentionLayNorm;
+    //////////////////
+    private final CodecBlock codecBlock;//前方的解码层
+    private CodecBlock lastEncoderBlock;//最后一层编码器
+
+    public void setLastEncoderBlock(CodecBlock lastEncoderBlock) {
+        this.lastEncoderBlock = lastEncoderBlock;
+    }
+
+    public FirstDecoderBlock(int multiNumber, int featureDimension, float studyPoint, CodecBlock codecBlock, int coreNumber,
+                             TransWordVector transWordVector) throws Exception {//进行初始化
+        //注意力层残差归一化
+        attentionLayNorm = new LayNorm(1, featureDimension, null, this, studyPoint, coreNumber, false
+                , 1);
+        multiSelfAttention = new MultiSelfAttention(multiNumber, studyPoint, 1, featureDimension, false, null,
+                coreNumber, transWordVector);
+        multiSelfAttention.setLayNorm(attentionLayNorm);
+        attentionLayNorm.setMultiSelfAttention(multiSelfAttention);
+        this.codecBlock = codecBlock;
+    }
+
+    public FirstDecoderModel getModel() throws Exception {
+        FirstDecoderModel firstDecoderModel = new FirstDecoderModel();
+        firstDecoderModel.setMultiSelfAttentionModel(multiSelfAttention.getModel());
+        firstDecoderModel.setAttentionLayNormModel(attentionLayNorm.getModel());
+        return firstDecoderModel;
+    }
+
+    public void insertModel(FirstDecoderModel firstDecoderModel) throws Exception {
+        multiSelfAttention.insertModel(firstDecoderModel.getMultiSelfAttentionModel());
+        attentionLayNorm.insertModel(firstDecoderModel.getAttentionLayNormModel());
+    }
+
+    public void backError(long eventID, Matrix error) throws Exception {
+        attentionLayNorm.backErrorFromLine(error, eventID);
+        lastEncoderBlock.encoderBackStart(eventID);
+    }
+
+    public void sendOutputMatrix(long eventID, Matrix out, boolean isStudy, OutBack outBack, List<Integer> E, boolean outAllPro) throws Exception {
+        Matrix c = lastEncoderBlock.getOutMatrix(eventID);
+        lastEncoderBlock.removeOutMatrix(eventID);
+        codecBlock.sendInputMatrix(eventID, out, isStudy, outBack, E, c, outAllPro);
+    }
+
+    //Decoder 参数正向入口
+    public void sendInputMatrix(long eventID, Matrix feature, boolean isStudy, OutBack outBack, List<Integer> E, boolean outAllPro) throws Exception {
+        multiSelfAttention.sendMatrixMessage(eventID, feature, isStudy, outBack, E, null, outAllPro);
+    }
+}