diff --git a/src/me/xiaosheng/word2vec/Test.java b/src/me/xiaosheng/word2vec/Test.java
index eb21912..07cc5ca 100644
--- a/src/me/xiaosheng/word2vec/Test.java
+++ b/src/me/xiaosheng/word2vec/Test.java
@@ -6,6 +6,8 @@
 import java.util.List;
 import java.util.Set;
 
+import com.ansj.vec.domain.WordEntry;
+
 public class Test {
 
 	public static void main(String[] args) {
@@ -17,16 +19,22 @@ public static void main(String[] args) {
 			// TODO Auto-generated catch block
 			e.printStackTrace();
 		}
+		//计算词语相似度
 		System.out.println(vec.wordSimilarity("狗", "猫"));
 		System.out.println(vec.wordSimilarity("计算机", "电脑"));
 		System.out.println(vec.wordSimilarity("计算机", "人"));
-		
+		//计算句子相似度
 		String s1 = "苏州 有 多条 公路 正在 施工 造成 局部 地区 汽车 行驶 非常 缓慢";
 		String s2 = "苏州 最近 有 多条 公路 在 施工 导致 部分 地区 交通 拥堵 汽车 难以 通行";
 		String s3 = "苏州 是 一座 美丽 的 城市 四季 分明 雨量 充沛";
 		System.out.println(vec.sentenceSimilairy(s1, s1));
 		System.out.println(vec.sentenceSimilairy(s1, s2));
 		System.out.println(vec.sentenceSimilairy(s1, s3));
+		//获取相似的词语
+		Set<WordEntry> similarWords = vec.getSimilarWords("漂亮", 10);
+		for(WordEntry word : similarWords) {
+			System.out.println(word.name + " : " + word.score);
+		}
 //		try {
 //			Word2Vec.trainJavaModel("data/train.txt", "data/test.model");
 //		} catch (IOException e) {
diff --git a/src/me/xiaosheng/word2vec/Word2Vec.java b/src/me/xiaosheng/word2vec/Word2Vec.java
index fa55b9e..e785bf5 100644
--- a/src/me/xiaosheng/word2vec/Word2Vec.java
+++ b/src/me/xiaosheng/word2vec/Word2Vec.java
@@ -3,13 +3,17 @@
 import java.io.File;
 import java.io.IOException;
 import java.util.Arrays;
+import java.util.Collections;
 import java.util.HashSet;
 import java.util.LinkedList;
 import java.util.List;
+import java.util.Map;
 import java.util.Set;
+import java.util.TreeSet;
 
 import com.ansj.vec.Learn;
 import com.ansj.vec.Word2VEC;
+import com.ansj.vec.domain.WordEntry;
 
 public class Word2Vec {
 
@@ -49,6 +53,19 @@ public float[] getWordVector(String word) {
 		}
 		return vec.getWordVector(word);
 	}
+	/**
+	 * 计算向量内积
+	 * @param vec1
+	 * @param vec2
+	 * @return
+	 */
+	private float calDist(float[] vec1, float[] vec2) {
+		float dist = 0;
+		for (int i = 0; i < vec1.length; i++) {
+			dist += vec1[i] * vec2[i];
+		}
+		return dist;
+	}
 	/**
 	 * 计算词相似度
 	 * @param word1
@@ -64,11 +81,41 @@ public float wordSimilarity(String word1, String word2) {
 		if(word1Vec == null || word2Vec == null) {
 			return -1;
 		}
-		float dist = 0;
-		for (int i = 0; i < word1Vec.length; i++) {
-			dist += word1Vec[i] * word2Vec[i];
+		return calDist(word1Vec, word2Vec);
+	}
+	/**
+	 * 获取相似词语
+	 * @param word
+	 * @param maxReturnNum
+	 * @return
+	 */
+	public Set<WordEntry> getSimilarWords(String word, int maxReturnNum) {
+		if (loadModel == false)
+			return null;
+		float[] center = getWordVector(word);
+		if (center == null) {
+			return Collections.emptySet();
 		}
-		return dist;
+		int resultSize = vec.getWords() < maxReturnNum ? vec.getWords() : maxReturnNum;
+		TreeSet<WordEntry> result = new TreeSet<WordEntry>();
+		double min = Double.MIN_VALUE;
+		for (Map.Entry<String, float[]> entry : vec.getWordMap().entrySet()) {
+			float[] vector = entry.getValue();
+			float dist = calDist(center, vector);
+			if (result.size() <= resultSize) {
+				result.add(new WordEntry(entry.getKey(), dist));
+				min = result.last().score;
+			} else {
+				if (dist > min) {
+					result.add(new WordEntry(entry.getKey(), dist));
+					result.pollLast();
+					min = result.last().score;
+				}
+			}
+		}
+		result.pollFirst();
+
+		return result;
 	}
 	/**
 	 * 计算句子相似度