diff --git a/src/main/java/org/dromara/easyai/transFormer/LineBlock.java b/src/main/java/org/dromara/easyai/transFormer/LineBlock.java
new file mode 100644
index 0000000..bd69ff8
--- /dev/null
+++ b/src/main/java/org/dromara/easyai/transFormer/LineBlock.java
@@ -0,0 +1,102 @@
+package org.dromara.easyai.transFormer;
+
+import org.dromara.easyai.function.ReLu;
+import org.dromara.easyai.function.Tanh;
+import org.dromara.easyai.i.OutBack;
+import org.dromara.easyai.matrixTools.Matrix;
+import org.dromara.easyai.matrixTools.MatrixOperation;
+import org.dromara.easyai.transFormer.model.LineBlockModel;
+import org.dromara.easyai.transFormer.nerve.HiddenNerve;
+import org.dromara.easyai.transFormer.nerve.Nerve;
+import org.dromara.easyai.transFormer.nerve.OutNerve;
+import org.dromara.easyai.transFormer.nerve.SoftMax;
+
+import java.util.ArrayList;
+import java.util.List;
+
+public class LineBlock {//线性层模块
+    private final List<HiddenNerve> hiddenNerveList = new ArrayList<>();
+    private final List<OutNerve> outNerveList = new ArrayList<>();//输出层
+    private final CodecBlock lastCodecBlock;//最后一层解码块
+    private Matrix allError;
+    private final int featureDimension;
+    private int backNumber = 0;//误差返回次数
+    private final MatrixOperation matrixOperation;
+
+    public LineBlockModel getModel() throws Exception {
+        LineBlockModel lineBlockModel = new LineBlockModel();
+        List<float[][]> hiddenNerveModel = new ArrayList<>();
+        List<float[][]> outNerveModel = new ArrayList<>();
+        for (HiddenNerve hiddenNerve : hiddenNerveList) {
+            hiddenNerveModel.add(hiddenNerve.getModel());
+        }
+        for (OutNerve outNerve : outNerveList) {
+            outNerveModel.add(outNerve.getModel());
+        }
+        lineBlockModel.setHiddenNervesModel(hiddenNerveModel);
+        lineBlockModel.setOutNervesModel(outNerveModel);
+        return lineBlockModel;
+    }
+
+    public void insertModel(LineBlockModel lineBlockModel) throws Exception {
+        List<float[][]> hiddenNerveModel = lineBlockModel.getHiddenNervesModel();
+        List<float[][]> outNerveModel = lineBlockModel.getOutNervesModel();
+        for (int i = 0; i < hiddenNerveList.size(); i++) {
+            hiddenNerveList.get(i).insertModel(hiddenNerveModel.get(i));
+        }
+        for (int i = 0; i < outNerveList.size(); i++) {
+            outNerveList.get(i).insertModel(outNerveModel.get(i));
+        }
+    }
+
+    public LineBlock(int typeNumber, int featureDimension, float studyPoint, CodecBlock lastCodecBlock,
+                     boolean showLog, int regularModel, float regular, int coreNumber, float timePunValue) throws Exception {
+        this.featureDimension = featureDimension;
+        this.lastCodecBlock = lastCodecBlock;
+        matrixOperation = new MatrixOperation(coreNumber);
+        SoftMax softMax = new SoftMax(outNerveList, showLog, typeNumber, typeNumber, typeNumber, timePunValue);
+        //隐层
+        List<Nerve> hiddenNerves = new ArrayList<>();
+        for (int i = 0; i < featureDimension; i++) {
+            HiddenNerve hiddenNerve = new HiddenNerve(i + 1, 1, studyPoint, new ReLu(), featureDimension,
+                    typeNumber, this, regularModel, regular, coreNumber);
+            hiddenNerves.add(hiddenNerve);
+            hiddenNerveList.add(hiddenNerve);
+        }
+        //输出层
+        List<Nerve> outNerves = new ArrayList<>();
+        for (int i = 0; i < typeNumber; i++) {
+            OutNerve outNerve = new OutNerve(i + 1, studyPoint, featureDimension, featureDimension, typeNumber, softMax
+                    , regularModel, regular, coreNumber);
+            outNerve.connectFather(hiddenNerves);
+            outNerves.add(outNerve);
+            outNerveList.add(outNerve);
+        }
+        for (Nerve nerve : hiddenNerves) {
+            nerve.connect(outNerves);
+        }
+    }
+
+    public void sendParameter(long eventID, Matrix feature, boolean isStudy, OutBack outBack, List<Integer> E, boolean outAllPro) throws Exception {
+        for (HiddenNerve hiddenNerve : hiddenNerveList) {
+            hiddenNerve.postMessage(eventID, feature, isStudy, outBack, E, outAllPro);
+        }
+    }
+
+    public void backError(long eventID, Matrix errorMatrix) throws Exception {//从线性层返回的误差
+        backNumber++;
+        if (allError == null) {
+            allError = errorMatrix;
+        } else {
+            allError = matrixOperation.add(errorMatrix, allError);
+        }
+        if (backNumber == featureDimension) {
+            backNumber = 0;
+            Matrix error = allError.getSonOfMatrix(0, 0, allError.getX(), allError.getY() - 1);
+            allError = null;
+            //将误差矩阵回传
+            lastCodecBlock.backError(eventID, error);
+        }
+    }
+
+}