diff --git a/src/main/java/org/dromara/easyai/rnnJumpNerveEntity/SoftMax.java b/src/main/java/org/dromara/easyai/rnnJumpNerveEntity/SoftMax.java
new file mode 100644
index 0000000..09d6c17
--- /dev/null
+++ b/src/main/java/org/dromara/easyai/rnnJumpNerveEntity/SoftMax.java
@@ -0,0 +1,122 @@
+package org.dromara.easyai.rnnJumpNerveEntity;
+
+
+import org.dromara.easyai.matrixTools.Matrix;
+import org.dromara.easyai.config.RZ;
+import org.dromara.easyai.i.OutBack;
+
+import java.util.ArrayList;
+import java.util.List;
+import java.util.Map;
+
+public class SoftMax extends Nerve {
+    private final List<OutNerve> outNerves;
+    private final boolean isShowLog;
+    private NerveCenter nerveCenter;//该输出层对应的神经中枢
+
+    public SoftMax(List<OutNerve> outNerves, boolean isShowLog
+            , int sensoryNerveNub, int hiddenNerveNub, int outNerveNub, int allDepth) throws Exception {
+        super(0, "softMax", 0, false, null
+                , RZ.NOT_RZ, 0, sensoryNerveNub, hiddenNerveNub, outNerveNub, allDepth
+                , false, 0);
+        this.outNerves = outNerves;
+        this.isShowLog = isShowLog;
+    }
+
+    public void setNerveCenter(NerveCenter nerveCenter) {
+        this.nerveCenter = nerveCenter;
+    }
+
+    @Override
+    protected void sendAppointSoftMax(long eventId, float parameter, Matrix featureMatrix, OutBack outBack, String myWord) throws Exception {
+        boolean allReady = insertParameter(eventId, parameter);
+        if (allReady) {
+            Mes mes = softMax(eventId, false);//输出值
+            destroyParameter(eventId);
+            nerveCenter.backType(eventId, mes.poi, mes.typeID, featureMatrix, outBack, myWord);
+        }
+    }
+
+    @Override
+    protected void input(long eventId, float parameter, boolean isStudy, Map<Integer, Float> E, OutBack outBack
+            , Matrix rnnMatrix, int[] storeys, int index, int questionLength) throws Exception {
+        boolean allReady = insertParameter(eventId, parameter);
+        if (allReady) {
+            Mes mes = softMax(eventId, isStudy);//输出值
+            int key = 0;
+            if (isStudy) {//学习
+                for (Map.Entry<Integer, Float> entry : E.entrySet()) {
+                    if (entry.getValue() > 0.9) {
+                        key = entry.getKey();
+                        break;
+                    }
+                }
+                if (isShowLog) {
+                    System.out.println("softMax==" + key + ",out==" + mes.poi + ",nerveId==" + mes.typeID);
+                }
+                List<Float> errors = error(mes, key);
+                features.remove(eventId); //清空当前上层输入参数参数
+                int size = outNerves.size();
+                for (int i = 0; i < size; i++) {
+                    outNerves.get(i).getGBySoftMax(errors.get(i), eventId, storeys, index);
+                }
+            } else {//输出
+                destroyParameter(eventId);
+                if (outBack != null) {
+                    outBack.getBack(mes.poi, mes.typeID, eventId);
+                    outBack.getSoftMaxBack(eventId, mes.softMax);
+                } else {
+                    throw new Exception("not find outBack");
+                }
+            }
+        }
+    }
+
+    private List<Float> error(Mes mes, int key) {
+        int t = key - 1;
+        List<Float> softMax = mes.softMax;
+        List<Float> error = new ArrayList<>();
+        for (int i = 0; i < softMax.size(); i++) {
+            float self = softMax.get(i);
+            float myError;
+            if (i != t) {
+                myError = -self;
+            } else {
+                myError = 1 - self;
+            }
+            error.add(myError);
+        }
+        return error;
+    }
+
+    private Mes softMax(long eventId, boolean isStudy) {//计算当前输出结果
+        float sigma = 0;
+        int id = 0;
+        float poi = 0;
+        Mes mes = new Mes();
+        List<Float> featuresList = features.get(eventId);
+        for (float value : featuresList) {
+            sigma = (float) Math.exp(value) + sigma;
+        }
+        List<Float> softMax = new ArrayList<>();
+        for (int i = 0; i < featuresList.size(); i++) {
+            float eSelf = (float) Math.exp(featuresList.get(i));
+            float value = eSelf / sigma;
+            softMax.add(value);
+            if (value > poi) {
+                poi = value;
+                id = i + 1;
+            }
+        }
+        mes.softMax = softMax;
+        mes.typeID = id;
+        mes.poi = poi;
+        return mes;
+    }
+
+    static class Mes {
+        int typeID;
+        float poi;
+        List<Float> softMax;
+    }
+}