diff --git a/src/main/java/org/dromara/easyai/naturalLanguage/word/WordEmbedding.java b/src/main/java/org/dromara/easyai/naturalLanguage/word/WordEmbedding.java
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+++ b/src/main/java/org/dromara/easyai/naturalLanguage/word/WordEmbedding.java
@@ -0,0 +1,175 @@
+package org.dromara.easyai.naturalLanguage.word;
+
+
+import org.dromara.easyai.matrixTools.Matrix;
+import org.dromara.easyai.matrixTools.MatrixList;
+import org.dromara.easyai.matrixTools.MatrixOperation;
+import org.dromara.easyai.config.RZ;
+import org.dromara.easyai.config.SentenceConfig;
+import org.dromara.easyai.entity.SentenceModel;
+import org.dromara.easyai.entity.WordMatrix;
+import org.dromara.easyai.entity.WordTwoVectorModel;
+import org.dromara.easyai.function.Tanh;
+import org.dromara.easyai.i.OutBack;
+import org.dromara.easyai.rnnJumpNerveEntity.MyWordFeature;
+import org.dromara.easyai.rnnNerveCenter.NerveManager;
+import org.dromara.easyai.rnnNerveEntity.SensoryNerve;
+
+import java.util.ArrayList;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+
+/**
+ * @param
+ * @DATA
+ * @Author LiDaPeng
+ * @Description 词嵌入向量训练
+ */
+public class WordEmbedding extends MatrixOperation {
+    private NerveManager nerveManager;
+    private SentenceModel sentenceModel;
+    private final List<String> wordList = new ArrayList<>();//单字集合
+    private SentenceConfig config;
+    private int wordVectorDimension;
+    private int studyTimes = 1;
+
+    public void setStudyTimes(int studyTimes) {
+        this.studyTimes = studyTimes;
+    }
+
+    public void setConfig(SentenceConfig config) {
+        this.config = config;
+    }
+
+    public int getWordVectorDimension() {
+        return wordVectorDimension;
+    }
+
+    public void init(SentenceModel sentenceModel, int wordVectorDimension) throws Exception {
+        this.wordVectorDimension = wordVectorDimension;
+        this.sentenceModel = sentenceModel;
+        wordList.addAll(sentenceModel.getWordSet());
+        nerveManager = new NerveManager(wordList.size(), wordVectorDimension, wordList.size()
+                , 1, new Tanh(), config.getWeStudyPoint(), config.getRzModel(),
+                config.getWeLParam());
+        nerveManager.init(true, false, true);
+    }
+
+    public List<String> getWordList() {
+        return wordList;
+    }
+
+    public String getWord(int id) {
+        return wordList.get(id);
+    }
+
+    public void insertModel(WordTwoVectorModel wordTwoVectorModel, int wordVectorDimension) throws Exception {
+        wordList.clear();
+        this.wordVectorDimension = wordVectorDimension;
+        List<String> myWordList = wordTwoVectorModel.getWordList();
+        wordList.addAll(myWordList);
+        nerveManager = new NerveManager(wordList.size(), wordVectorDimension, wordList.size()
+                , 1, new Tanh(), config.getWeStudyPoint(), RZ.NOT_RZ, 0);
+        nerveManager.init(true, false, true);
+        nerveManager.insertModelParameter(wordTwoVectorModel.getModelParameter());
+    }
+
+    public MyWordFeature getEmbedding(String word, long eventId, boolean once) throws Exception {//做截断
+        MyWordFeature myWordFeature = new MyWordFeature();
+        int wordDim = wordVectorDimension;//
+        MatrixList matrixList = null;
+        for (int i = 0; i < word.length(); i++) {
+            WordMatrix wordMatrix = new WordMatrix(wordDim);
+            String myWord;
+            if (!once) {
+                myWord = word.substring(i, i + 1);
+            } else {
+                myWord = word;
+            }
+            int index = getID(myWord);
+            studyDNN(eventId, index, 0, wordMatrix, false);
+            if (matrixList == null) {
+                myWordFeature.setFirstFeatureList(wordMatrix.getList());
+                matrixList = new MatrixList(wordMatrix.getVector(), true);
+            } else {
+                matrixList.add(wordMatrix.getVector());
+            }
+            if (once) {
+                break;
+            }
+        }
+        myWordFeature.setFeatureMatrix(matrixList.getMatrix());
+        return myWordFeature;
+    }
+
+    private void studyDNN(long eventId, int featureIndex, int resIndex, OutBack outBack, boolean isStudy) throws Exception {
+        List<SensoryNerve> sensoryNerves = nerveManager.getSensoryNerves();
+        int size = sensoryNerves.size();
+        Map<Integer, Float> map = new HashMap<>();
+        if (resIndex > 0) {
+            map.put(resIndex + 1, 1f);
+        }
+        for (int i = 0; i < size; i++) {
+            float feature = 0;
+            if (i == featureIndex) {
+                feature = 1;
+            }
+            sensoryNerves.get(i).postMessage(eventId, feature, isStudy, map, outBack, true, null);
+        }
+    }
+
+
+    public WordTwoVectorModel start() throws Exception {//开始进行词向量训练
+        List<String[]> sentenceList = sentenceModel.getSentenceList();
+        int size = sentenceList.size();
+        System.out.println("词嵌入训练启动...");
+        int allTimes = studyTimes * size;
+        int index = 0;
+        for (int k = 0; k < studyTimes; k++) {
+            for (int i = 0; i < size; i++) {
+                index++;
+                long start = System.currentTimeMillis();
+                study(sentenceList.get(i));
+                long end = System.currentTimeMillis() - start;
+                float r = (float) index / allTimes * 100;
+                String result = String.format("%.6f", r);
+                System.out.println("size:" + size + ",index:" + i + ",耗时:" + end + ",完成度:" + result + "%");
+            }
+        }
+        WordTwoVectorModel wordTwoVectorModel = new WordTwoVectorModel();
+        wordTwoVectorModel.setModelParameter(nerveManager.getModelParameter());
+        wordTwoVectorModel.setWordList(wordList);
+        //词向量训练结束
+        return wordTwoVectorModel;
+    }
+
+    private void study(String[] word) throws Exception {
+        int[] indexArray = new int[word.length];
+        for (int i = 0; i < word.length; i++) {
+            int index = getID(word[i]);
+            indexArray[i] = index;
+        }
+        for (int i = 0; i < indexArray.length; i++) {
+            int index = indexArray[i];
+            for (int j = 0; j < indexArray.length; j++) {
+                if (i != j) {
+                    int resIndex = indexArray[j];
+                    studyDNN(1, index, resIndex, null, true);
+                }
+            }
+        }
+    }
+
+    public int getID(String word) {
+        int index = 0;
+        int size = wordList.size();
+        for (int i = 0; i < size; i++) {
+            if (wordList.get(i).equals(word)) {
+                index = i;
+                break;
+            }
+        }
+        return index;
+    }
+}