diff --git a/BotSharp.NLP.UnitTest/Vector/Word2VecTest.cs b/BotSharp.NLP.UnitTest/Vector/Word2VecTest.cs
new file mode 100644
index 00000000..994fa78f
--- /dev/null
+++ b/BotSharp.NLP.UnitTest/Vector/Word2VecTest.cs
@@ -0,0 +1,31 @@
+using BotSharp.NLP.Txt2Vec;
+using Microsoft.Extensions.Configuration;
+using Microsoft.VisualStudio.TestTools.UnitTesting;
+using System;
+using System.Collections.Generic;
+using System.IO;
+using System.Linq;
+using System.Text;
+using Txt2Vec;
+
+namespace BotSharp.NLP.UnitTest.Vector
+{
+    public class Word2VecTest
+    {
+        [TestClass]
+        public class OneHotEncodingTest : TestEssential
+        {
+            [TestMethod]
+            public void Word2VecTest()
+            {
+                string sentence = "stop this song";
+                List<string> words = sentence.Split(' ').ToList();
+                Args args = new Args();
+                args.ModelFile = @"C:\Users\bpeng\Desktop\BoloReborn\Txt2VecDemo\wordvec_enu.bin";
+                VectorGenerator vg = new VectorGenerator(args);
+
+                vg.Distance(words);
+            }
+        }
+    }
+}
diff --git a/BotSharp.NLP/Classify/ClassifyOptions.cs b/BotSharp.NLP/Classify/ClassifyOptions.cs
index 35671344..1e0b68f4 100644
--- a/BotSharp.NLP/Classify/ClassifyOptions.cs
+++ b/BotSharp.NLP/Classify/ClassifyOptions.cs
@@ -13,6 +13,7 @@ namespace BotSharp.NLP.Classify
         public string ModelName { get; set; }
 
         public string FeaturesFileName { get; set; }
+        public string FeaturesInTfIdfFileName { get; set; }
         public string DictionaryFileName { get; set; }
         public string CategoriesFileName { get; set; }
 
diff --git a/BotSharp.NLP/Classify/SVMClassifier.cs b/BotSharp.NLP/Classify/SVMClassifier.cs
index 124a49ea..9539622b 100644
--- a/BotSharp.NLP/Classify/SVMClassifier.cs
+++ b/BotSharp.NLP/Classify/SVMClassifier.cs
@@ -40,6 +40,7 @@ namespace BotSharp.NLP.Classify
         private List<string> categories;
         private RangeTransform transform;
         private Bigtree.Algorithm.SVM.Model model;
+        private List<string> featuresInTfIdf;
 
         public void Train(List<Sentence> sentences, ClassifyOptions options)
         {
@@ -48,6 +49,12 @@ namespace BotSharp.NLP.Classify
 
         public void SVMClassifierTrain(List<Sentence> sentences, ClassifyOptions options, SvmType svm = SvmType.C_SVC, KernelType kernel = KernelType.RBF, bool probability = true, string outputFile = null)
         {
+            var tfidf = new TfIdfFeatureExtractor();
+            tfidf.Dimension = options.Dimension;
+            tfidf.Sentences = sentences;
+            tfidf.CalBasedOnCategory();
+            featuresInTfIdf = tfidf.Keywords();
+
             // copy test multiclass Model
             Problem train = new Problem();
             train.X = GetData(sentences).ToArray();
@@ -124,7 +131,7 @@ namespace BotSharp.NLP.Classify
 
         public List<Node[]> GetData(List<Sentence> sentences)
         {
-            // var extractor = new CountFeatureExtractor();
+            //var extractor = new CountFeatureExtractor();
             var extractor = new Word2VecFeatureExtractor();
             extractor.Sentences = sentences;
             if(features != null)
@@ -137,7 +144,7 @@ namespace BotSharp.NLP.Classify
                 extractor.Dictionary = dictionary;
             }
 
-            extractor.Vectorize();
+            extractor.Vectorize(featuresInTfIdf);
 
             if(features == null)
             {
@@ -157,8 +164,9 @@ namespace BotSharp.NLP.Classify
 
                 for(int i = 0; i < extractor.Features.Count; i++)
                 {
+
                     int name = i;
-                    var xx = sentence.Words.Find(x => x.Lemma == extractor.Features[i]);
+                    /*var xx = sentence.Words.Find(x => x.Lemma == extractor.Features[i]);
 
                     if (xx == null)
                     {
@@ -167,7 +175,9 @@ namespace BotSharp.NLP.Classify
                     else
                     {
                         curNodes.Add(new Node(name, xx.Vector));
-                    }
+                    }*/
+
+                   curNodes.Add(new Node(i, sentence.Vector[i]));
                 }
 
                 datas.Add(curNodes.ToArray());
@@ -181,9 +191,12 @@ namespace BotSharp.NLP.Classify
             options.FeaturesFileName = Path.Combine(options.ModelDir, "features");
             options.DictionaryFileName = Path.Combine(options.ModelDir, "dictionary");
             options.CategoriesFileName = Path.Combine(options.ModelDir, "categories");
+            options.FeaturesInTfIdfFileName = Path.Combine(options.ModelDir, "featuresInTfIdf");
 
             File.WriteAllText(options.FeaturesFileName, JsonConvert.SerializeObject(features));
 
+            File.WriteAllText(options.FeaturesInTfIdfFileName, JsonConvert.SerializeObject(featuresInTfIdf));
+
             File.WriteAllText(options.DictionaryFileName, JsonConvert.SerializeObject(dictionary));
 
             File.WriteAllText(options.CategoriesFileName, JsonConvert.SerializeObject(categories));
@@ -201,9 +214,12 @@ namespace BotSharp.NLP.Classify
             options.ModelFilePath = Path.Combine(options.ModelDir, options.ModelName);
             options.TransformFilePath = Path.Combine(options.ModelDir, "transform");
             options.CategoriesFileName = Path.Combine(options.ModelDir, "categories");
+            options.FeaturesInTfIdfFileName = Path.Combine(options.ModelDir, "featuresInTfIdf");
 
             features = JsonConvert.DeserializeObject<List<String>>(File.ReadAllText(options.FeaturesFileName));
 
+            featuresInTfIdf = JsonConvert.DeserializeObject<List<String>>(File.ReadAllText(options.FeaturesInTfIdfFileName));
+
             dictionary = JsonConvert.DeserializeObject<List<Tuple<string, int>>>(File.ReadAllText(options.DictionaryFileName));
 
             categories = JsonConvert.DeserializeObject<List<String>>(File.ReadAllText(options.CategoriesFileName));
diff --git a/BotSharp.NLP/Featuring/CountFeatureExtractor.cs b/BotSharp.NLP/Featuring/CountFeatureExtractor.cs
index be8a0992..40e438f4 100644
--- a/BotSharp.NLP/Featuring/CountFeatureExtractor.cs
+++ b/BotSharp.NLP/Featuring/CountFeatureExtractor.cs
@@ -37,7 +37,7 @@ namespace BotSharp.NLP.Featuring
         public List<string> Features { get; set; }
         public Shape Shape { get; set; }
 
-        public void Vectorize()
+        public void Vectorize(List<string> features)
         {
             CalculateDictionary();
 
diff --git a/BotSharp.NLP/Featuring/IFeatureExtractor.cs b/BotSharp.NLP/Featuring/IFeatureExtractor.cs
index a3d6559f..024edea5 100644
--- a/BotSharp.NLP/Featuring/IFeatureExtractor.cs
+++ b/BotSharp.NLP/Featuring/IFeatureExtractor.cs
@@ -30,7 +30,7 @@ namespace BotSharp.NLP.Featuring
         /// <summary>
         /// Vectorize sentence
         /// </summary>
-        void Vectorize();
+        void Vectorize(List<string> features);
 
         /// <summary>
         /// Array shape
diff --git a/BotSharp.NLP/Featuring/TfIdfFeatureExtractor.cs b/BotSharp.NLP/Featuring/TfIdfFeatureExtractor.cs
index 9f9a01bf..66319597 100644
--- a/BotSharp.NLP/Featuring/TfIdfFeatureExtractor.cs
+++ b/BotSharp.NLP/Featuring/TfIdfFeatureExtractor.cs
@@ -192,7 +192,7 @@ namespace BotSharp.NLP.Featuring
             return result;
         }
 
-        public void Vectorize()
+        public void Vectorize(List<string> features)
         {
             throw new NotImplementedException();
         }
diff --git a/BotSharp.NLP/Featuring/Word2VecFeatureExtractor.cs b/BotSharp.NLP/Featuring/Word2VecFeatureExtractor.cs
index bf6e2101..c71ddbea 100644
--- a/BotSharp.NLP/Featuring/Word2VecFeatureExtractor.cs
+++ b/BotSharp.NLP/Featuring/Word2VecFeatureExtractor.cs
@@ -18,30 +18,48 @@ namespace BotSharp.NLP.Featuring
 
         public Word2VecFeatureExtractor()
         {
-            Args args = new Args();
-            args.ModelFile = "C:\\Users\\bpeng\\Desktop\\BoloReborn\\BotSharp\\BotSharp.WebHost\\App_Data\\wordvec_enu.bin";
-            this.Vg = new VectorGenerator(args);
-            this.SentenceVectorSize = this.Vg.Model.VectorSize * MaxSentenceTokenCounts();
+
         }
 
-        public void Vectorize()
+        public void Vectorize(List<string> features)
         {
+            Init();
+
             Sentences.ForEach(s => {
-                Vec sentenceVec = new Vec();
+                List<string> wordLemmas = new List<string>();
                 s.Words.ForEach(word => {
-                    Vec wordVec = Vg.Word2Vec(word.Text);
-                    sentenceVec.VecNodes.AddRange(wordVec.VecNodes);
+                    if (features.Contains(word.Lemma))
+                    {
+                        wordLemmas.Add(word.Lemma);
+                    }
                 });
-                while (sentenceVec.VecNodes.Count != SentenceVectorSize)
-                {
-                    sentenceVec.VecNodes.Add(0);
-                }
+                Vec sentenceVec = Vg.Sent2Vec(wordLemmas);
+
                 s.Vector = sentenceVec.VecNodes.ToArray();
             });
+
+
+        }
+
+        private void Init()
+        {
+            if(Vg == null)
+            {
+                Args args = new Args();
+                args.ModelFile = @"C:\Users\bpeng\Desktop\BoloReborn\Txt2VecDemo\wordvec_enu.bin";
+                Vg = new VectorGenerator(args);
+                SentenceVectorSize = this.Vg.Model.VectorSize * MaxSentenceTokenCounts();
+                Features = new List<string>();
+                for (int i = 0; i < SentenceVectorSize; i++)
+                {
+                    Features.Add($"f-{i}");
+                }
+            }
         }
 
         private int MaxSentenceTokenCounts()
         {
+            return 1;
             int maxCount = 0;
             Sentences.ForEach(s=> {
                 if (s.Words.Count > maxCount)
diff --git a/BotSharp.NLP/Txt2Vec/VectorGenerator.cs b/BotSharp.NLP/Txt2Vec/VectorGenerator.cs
index fbf23aef..b54bb642 100644
--- a/BotSharp.NLP/Txt2Vec/VectorGenerator.cs
+++ b/BotSharp.NLP/Txt2Vec/VectorGenerator.cs
@@ -169,6 +169,30 @@ namespace Txt2Vec
             return vec;
         }
 
+        public Vec Sent2Vec(List<string> words)
+        {
+            Vec vec = new Vec();
+
+            Txt2Vec.Decoder decoder = new Txt2Vec.Decoder(Model);
+            string[] termList = words.ToArray();
+            vec.VecNodes = decoder.ToVector(termList).ToList();
+
+            return vec;
+        }
+
+        public void Distance(List<string> words)
+        {
+
+            Txt2Vec.Decoder decoder = new Txt2Vec.Decoder(Model);
+            words.ForEach(word=> {
+                Console.WriteLine($"current word: {word}");
+                List<Result> sysnonyms = decoder.Distance(word);
+                sysnonyms.ForEach(s=> {
+                    Console.WriteLine($"{s.strTerm}: {s.score}");
+                });
+            });
+        }
+
         public double Similarity(Vec vec1, Vec vec2)
         {
             double score = 0;