diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..9aab07f
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,19 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+
+# PyBuilder
+target/
+.idea
+dataset/*
+sentence/*
+!.keep
+
+# vector cache
+vector_cache/
+
+# model cache
+model_dir/
+
+# feature cache
+feature_dir/
\ No newline at end of file
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..82443f5
--- /dev/null
+++ b/README.md
@@ -0,0 +1,58 @@
+# PascalSentenceDataset
+
+This program is utility to download pascal sentence dataset.
+
+## Installation
+
+You can install by "git clone" command.
+
+```
+git clone https://github.com/rupy/PascalSentenceDataset.git
+```
+
+### Dependency
+
+You must install some python libraries. Use pip command. Python>=2
+
+```
+PyQuery
+```
+
+## Usage
+
+To download dataset, just run program as follow:
+
+```
+python pascal_sentence_dataset.py
+```
+
+You can also write code like this:
+
+```python
+# import
+from pascal_sentence_dataset import PascalSentenceDataSet
+
+# create instance
+dataset = PascalSentenceDataSet()
+# download images
+dataset.download_images()
+# download sentences
+dataset.download_sentences()
+# create correspondence data by dataset
+# dataset.create_correspondence_data()
+
+# create my pair data
+dataset.create_pair_data()
+# preprocess data
+dataset.preprocess_data()
+```
+
+Return the following file list: (./list/)
+- _correspondence.csv_ 1000 list data, titled: index, image
+- _data_pairs.csv_ 1000 list data, titled: index, image, text, label
+- _train.csv_ the training set with 800 image-text pairs (40 pairs per class)
+- _validate.csv_ 100 the validation set with 100 image-text pairs (5 pairs per class)
+- _test.csv_ 100 the testing set with 100 image-text pairs (5 pairs per class)
+
+
+
diff --git a/dataset.py b/dataset.py
new file mode 100644
index 0000000..6dd7b0c
--- /dev/null
+++ b/dataset.py
@@ -0,0 +1,65 @@
+import torch
+
+import torch.nn as nn
+from torchvision import transforms
+from PIL import Image
+from torchtext import data
+from torchtext.data import get_tokenizer
+from torch.nn import init
+
+
+
+
+
+# VGG模型提取图像特征
+class VGG:
+    def __init__(self):
+        super().__init__()
+
+        # load image model
+        image_model = torch.hub.load('pytorch/vision:v0.6.0', 'vgg19', pretrained=True)
+        # or any of these variants
+        # model = torch.hub.load('pytorch/vision:v0.6.0', 'vgg11_bn', pretrained=True)
+        # model = torch.hub.load('pytorch/vision:v0.6.0', 'vgg13', pretrained=True)
+        # model = torch.hub.load('pytorch/vision:v0.6.0', 'vgg13_bn', pretrained=True)
+        # model = torch.hub.load('pytorch/vision:v0.6.0', 'vgg16', pretrained=True)
+        # model = torch.hub.load('pytorch/vision:v0.6.0', 'vgg16_bn', pretrained=True)
+        # model = torch.hub.load('pytorch/vision:v0.6.0', 'vgg19', pretrained=True)
+        # model = torch.hub.load('pytorch/vision:v0.6.0', 'vgg19_bn', pretrained=True)
+
+        image_model.eval()  # 处于验证状态
+        image_model.classifier._modules['6'] = nn.Identity()  # update the last layer
+        self.image_model = image_model.to(DEVICE)
+
+        # set image transform
+        self.preprocess = transforms.Compose([
+            transforms.Resize(256),
+            transforms.CenterCrop(224),
+            transforms.ToTensor(),
+            transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+        ])
+
+    # 裁剪图片，输出为1×3×224×224
+    def transform_image(self, image):
+        input_tensor = self.preprocess(image)
+        return input_tensor.unsqueeze(0)  # create a mini-batch as expected by the model
+
+    # 获取图片特征，输出为4096维
+    def feature_image(self, batch):
+        batch = batch.to(DEVICE)
+        with torch.no_grad():
+            return self.image_model(batch)
+
+
+# 获取图像的VGG特征
+def get_image_tokenize(image_file):
+    input_image = Image.open('./dataset/' + image_file)
+    input_batch = vgg19.transform_image(input_image)
+    return vgg19.feature_image(input_batch)
+
+
+# 可用设备
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# 初始化VGG模型
+vgg19 = VGG()
diff --git a/list/correspondence.csv b/list/correspondence.csv
new file mode 100644
index 0000000..7bf9360
--- /dev/null
+++ b/list/correspondence.csv
@@ -0,0 +1,1000 @@
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diff --git a/list/data_pairs.csv b/list/data_pairs.csv
new file mode 100644
index 0000000..6ae4007
--- /dev/null
+++ b/list/data_pairs.csv
@@ -0,0 +1,1001 @@
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diff --git a/list/test.csv b/list/test.csv
new file mode 100644
index 0000000..36872da
--- /dev/null
+++ b/list/test.csv
@@ -0,0 +1,101 @@
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diff --git a/list/train.csv b/list/train.csv
new file mode 100644
index 0000000..6687ecc
--- /dev/null
+++ b/list/train.csv
@@ -0,0 +1,801 @@
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+549,549,diningtable/2008_008388.jpg,diningtable/2008_008388txt,11
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+341,341,car/2008_006827.jpg,car/2008_006827txt,7
+179,179,boat/2008_005683.jpg,boat/2008_005683txt,4
+991,991,tvmonitor/2008_007196.jpg,tvmonitor/2008_007196txt,20
+561,561,dog/2008_001479.jpg,dog/2008_001479txt,12
+397,397,cat/2008_007589.jpg,cat/2008_007589txt,8
+209,209,bottle/2008_001486.jpg,bottle/2008_001486txt,5
+273,273,bus/2008_005277.jpg,bus/2008_005277txt,6
+693,693,motorbike/2008_008177.jpg,motorbike/2008_008177txt,14
+672,672,motorbike/2008_005902.jpg,motorbike/2008_005902txt,14
+657,657,motorbike/2008_001691.jpg,motorbike/2008_001691txt,14
diff --git a/list/val.csv b/list/val.csv
new file mode 100644
index 0000000..5cf1e44
--- /dev/null
+++ b/list/val.csv
@@ -0,0 +1,101 @@
+,index,image,text,label
+236,236,bottle/2008_006182.jpg,bottle/2008_006182txt,5
+600,600,horse/2008_000219.jpg,horse/2008_000219txt,13
+475,475,cow/2008_006547.jpg,cow/2008_006547txt,10
+242,242,bottle/2008_007147.jpg,bottle/2008_007147txt,5
+998,998,tvmonitor/2008_008439.jpg,tvmonitor/2008_008439txt,20
+281,281,bus/2008_006635.jpg,bus/2008_006635txt,6
+938,938,train/2008_008200.jpg,train/2008_008200txt,19
+564,564,dog/2008_002395.jpg,dog/2008_002395txt,12
+656,656,motorbike/2008_001586.jpg,motorbike/2008_001586txt,14
+8,8,aeroplane/2008_002358.jpg,aeroplane/2008_002358txt,1
+500,500,diningtable/2008_000043.jpg,diningtable/2008_000043txt,11
+932,932,train/2008_007759.jpg,train/2008_007759txt,19
+414,414,chair/2008_002521.jpg,chair/2008_002521txt,9
+804,804,sheep/2008_001601.jpg,sheep/2008_001601txt,17
+456,456,cow/2008_001359.jpg,cow/2008_001359txt,10
+415,415,chair/2008_002613.jpg,chair/2008_002613txt,9
+181,181,boat/2008_005720.jpg,boat/2008_005720txt,4
+527,527,diningtable/2008_004588.jpg,diningtable/2008_004588txt,11
+0,0,aeroplane/2008_000716.jpg,aeroplane/2008_000716txt,1
+320,320,car/2008_003415.jpg,car/2008_003415txt,7
+253,253,bus/2008_001820.jpg,bus/2008_001820txt,6
+597,597,dog/2008_007567.jpg,dog/2008_007567txt,12
+930,930,train/2008_007280.jpg,train/2008_007280txt,19
+900,900,train/2008_000343.jpg,train/2008_000343txt,19
+239,239,bottle/2008_006890.jpg,bottle/2008_006890txt,5
+399,399,cat/2008_007888.jpg,cat/2008_007888txt,8
+737,737,person/2008_005107.jpg,person/2008_005107txt,15
+73,73,bicycle/2008_005276.jpg,bicycle/2008_005276txt,2
+214,214,bottle/2008_003182.jpg,bottle/2008_003182txt,5
+17,17,aeroplane/2008_004165.jpg,aeroplane/2008_004165txt,1
+708,708,person/2008_000834.jpg,person/2008_000834txt,15
+634,634,horse/2008_007612.jpg,horse/2008_007612txt,13
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+213,213,bottle/2008_002870.jpg,bottle/2008_002870txt,5
+785,785,pottedplant/2008_007281.jpg,pottedplant/2008_007281txt,16
+519,519,diningtable/2008_003534.jpg,diningtable/2008_003534txt,11
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+598,598,dog/2008_007694.jpg,dog/2008_007694txt,12
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+865,865,sofa/2008_002922.jpg,sofa/2008_002922txt,18
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+74,74,bicycle/2008_006064.jpg,bicycle/2008_006064txt,2
+599,599,dog/2008_007871.jpg,dog/2008_007871txt,12
+763,763,pottedplant/2008_002882.jpg,pottedplant/2008_002882txt,16
+984,984,tvmonitor/2008_005660.jpg,tvmonitor/2008_005660txt,20
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+677,677,motorbike/2008_006526.jpg,motorbike/2008_006526txt,14
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+653,653,motorbike/2008_000328.jpg,motorbike/2008_000328txt,14
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+861,861,sofa/2008_001869.jpg,sofa/2008_001869txt,18
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+128,128,bird/2008_006186.jpg,bird/2008_006186txt,3
+449,449,chair/2008_008593.jpg,chair/2008_008593txt,9
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+904,904,train/2008_001850.jpg,train/2008_001850txt,19
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+754,754,pottedplant/2008_000960.jpg,pottedplant/2008_000960txt,16
+70,70,bicycle/2008_004656.jpg,bicycle/2008_004656txt,2
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+36,36,aeroplane/2008_008044.jpg,aeroplane/2008_008044txt,1
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+603,603,horse/2008_001235.jpg,horse/2008_001235txt,13
+108,108,bird/2008_003160.jpg,bird/2008_003160txt,3
+446,446,chair/2008_007949.jpg,chair/2008_007949txt,9
+150,150,boat/2008_000195.jpg,boat/2008_000195txt,4
+69,69,bicycle/2008_004654.jpg,bicycle/2008_004654txt,2
+371,371,cat/2008_003772.jpg,cat/2008_003772txt,8
+486,486,cow/2008_008121.jpg,cow/2008_008121txt,10
+514,514,diningtable/2008_002384.jpg,diningtable/2008_002384txt,11
+164,164,boat/2008_003100.jpg,boat/2008_003100txt,4
diff --git a/pascal_sentence_dataset.py b/pascal_sentence_dataset.py
new file mode 100644
index 0000000..5d09283
--- /dev/null
+++ b/pascal_sentence_dataset.py
@@ -0,0 +1,131 @@
+from urllib.parse import urljoin
+from pyquery import PyQuery
+import os
+import requests
+import csv
+import pandas as pd
+from sklearn.model_selection import train_test_split
+
+
+class PascalSentenceDataSet:
+    DATASET_DIR = 'dataset/'
+    SENTENCE_DIR = 'sentence/'
+    PASCAL_SENTENCE_DATASET_URL = 'http://vision.cs.uiuc.edu/pascal-sentences/'
+
+    def __init__(self):
+        self.url = PascalSentenceDataSet.PASCAL_SENTENCE_DATASET_URL
+
+    def download_images(self):
+        dom = PyQuery(self.url)
+        for img in dom('img').items():
+            img_src = img.attr['src']
+            category, img_file_name = os.path.split(img_src)
+
+            # make category directories
+            output_dir = PascalSentenceDataSet.DATASET_DIR + category
+            if not os.path.isdir(output_dir):
+                os.mkdir(output_dir)
+
+            # download image
+            output = os.path.join(output_dir, img_file_name)
+            print(output)
+            if img_src.startswith('http'):
+                img_url = img_src
+            else:
+                img_url = urljoin(self.url, img_src)
+            if os.path.isfile(output):
+                print("Already downloaded, Skipping: %s" % output)
+                continue
+            print("Downloading: %s" % output)
+            with open(output, 'wb') as f:
+
+                while True:
+                    result = requests.get(img_url)
+                    raw = result.content
+                    if result.status_code == 200:
+                        f.write(raw)
+                        break
+                    print("error occurred while fetching img")
+                    print("retry...")
+
+    def download_sentences(self):
+        dom = PyQuery(self.url)
+        # tbody disappears in pyquery DOM
+        for tr in dom('body>table>tr').items():
+            img_src = tr('img').attr['src']
+            category, img_file_name = os.path.split(img_src)
+
+            # make category directories
+            output_dir = PascalSentenceDataSet.SENTENCE_DIR + category
+            if not os.path.isdir(output_dir):
+                os.mkdir(output_dir)
+
+            # download sentences
+            head, tail = os.path.splitext(img_file_name)
+            sentence_file_name = head + "txt"
+            output = os.path.join(output_dir, sentence_file_name)
+            if os.path.isfile(output):
+                print("Already downloaded, Skipping: %s" % output)
+                continue
+            print("Downloading: %s" % output)
+            with open(output, 'w') as f:
+                for td in tr('table tr td').items():
+                    f.write(td.text() + "\n")
+
+    def create_correspondence_data(self):
+        dom = PyQuery(self.url)
+        writer = csv.writer(open('list/correspondence.csv', 'wb'))
+        for i, img in enumerate(dom('img').items()):
+            img_src = img.attr['src']
+            print("%d => %s" % (i + 1, img_src))
+            writer.writerow([i + 1, img_src])
+
+    # my create pair data
+    def create_pair_data(self):
+        dom = PyQuery(self.url)
+        writer = csv.writer(open('list/data_pairs.csv', 'w', newline=''))   # newline 每次添加时不增加新空行
+        writer.writerow(['index', 'image', 'text', 'label'])
+        category = ''
+        cat_count = 0
+        for i, img in enumerate(dom('img').items()):    # enumerate() 可以迭代返回计数
+            img_src = img.attr['src']
+            print("%d => %s" % (i, img_src))
+            current_cat = img_src.split('/')[0]         # 当前分类
+            if category != current_cat:                 # 判断是否是同一个类别
+                cat_count += 1
+                category = current_cat
+            txt = img_src.replace('.jpg', 'txt')        # 获取文本
+            writer.writerow([i, img_src, txt, cat_count])
+
+    # 预处理数据，参考Demo：move-review
+    def preprocess_data(self):
+        # 读取csv文件
+        # 有标题的直接通过data_list['title']的标题获取列数据
+        # 没有标题，可以通过data_list['0']的index获取列数据
+        data_list = pd.read_csv('list/data_pairs.csv')
+
+        # 将数据分成0.8的训练集和0.2的另一数据集，以label标签为分层依据
+        train, temp_list = train_test_split(data_list, test_size=0.2, stratify=data_list['label'])
+
+        # 将原数据的0.2（另一数据集），分成测试机和验证集，同样以label标签作为分层依据
+        val, test = train_test_split(temp_list, test_size=0.5, stratify=temp_list['label'])
+
+        # 将文件保存到本地
+        train.to_csv('./list/train.csv')
+        val.to_csv('./list/val.csv')
+        test.to_csv('./list/test.csv')
+
+
+if __name__ == "__main__":
+    # create instance
+    dataset = PascalSentenceDataSet()
+    # download images
+    dataset.download_images()
+    # download sentences
+    dataset.download_sentences()
+    # create correspondence data by dataset
+    dataset.create_correspondence_data()
+    # create my pair data
+    dataset.create_pair_data()
+    # preprocess data
+    dataset.preprocess_data()
diff --git a/textcnn_data_feature.py b/textcnn_data_feature.py
new file mode 100644
index 0000000..277089f
--- /dev/null
+++ b/textcnn_data_feature.py
@@ -0,0 +1,55 @@
+import torch
+import torch.nn as nn
+import os
+import scipy.io as io
+import numpy as np
+
+
+class TextCNNDataFeature:
+    def __init__(self, model, args):
+        super(TextCNNDataFeature, self).__init__()
+
+        self.model_dir = args.model_save_dir        # 模型存储路径
+        self.feature_dir = args.feature_save_dir    # 特征存储路径
+
+        model.load_state_dict(torch.load('./{}/best_steps.pt'.format(self.model_dir)))
+        model.eval()
+        model.dropout = nn.Identity()   # 随机丢失，设置为直接输出
+        model.linear = nn.Identity()    # 线性映射，设置为直接输出
+
+        self.model = model
+
+    # 获取数据集特征
+    def get_features_labels(self, data_iter):
+        feature_list = []                                               # 数据类型为 List 列表
+        label_list = []
+        for batch in data_iter:
+            with torch.no_grad():
+                feature, label = batch.text.t_(), batch.label
+            feature_tensor = self.model(feature)                        # 提取数据的特征，数据类型为 Tensor
+            feature_numpy = feature_tensor.cuda().data.cpu().numpy()    # 转换数据格式，数据类型为 numpy 数组
+            feature_list.append(feature_numpy)                          # 转换数据格式，数据类型为 List numpy 数组
+            label_numpy = label.cuda().data.cpu().numpy()               # 标签特征
+            label_list.append(label_numpy)
+
+        features = np.array(feature_list)                               # 数据类型为 np.array
+        features = np.reshape(features, (-1, features.shape[2]))        # 原向量：2×50×300，现在将最后一维固定，合并为100×300
+        labels = np.array(label_list)
+        labels = np.reshape(labels, (-1, 1))
+        return features, labels
+
+    # 保存特征和标签
+    def save_features_labels(self, train_iter, val_iter, test_iter):
+        f_train, f_tra_label = self.get_features_labels(train_iter)  # 获取训练特征
+        f_val, f_val_label = self.get_features_labels(val_iter)      # 获取验证特征
+        f_test, f_tes_label = self.get_features_labels(test_iter)    # 获取测试特征
+        if not os.path.isdir(self.feature_dir):
+            os.makedirs(self.feature_dir)
+        save_name = 'feature.mat'
+        save_path = os.path.join(self.feature_dir, save_name)
+        io.savemat(save_path, {
+            'T_tra_CNN': f_train, 'T_val_CNN': f_val, 'T_tes_CNN': f_test,
+            'T_tra_CNN_label': f_tra_label, 'T_val_CNN_label': f_val_label, 'T_tes_CNN_label': f_tes_label
+        })
+
+
diff --git a/textcnn_data_processer.py b/textcnn_data_processer.py
new file mode 100644
index 0000000..26e96b0
--- /dev/null
+++ b/textcnn_data_processer.py
@@ -0,0 +1,102 @@
+from torchtext.data import get_tokenizer
+from torchtext import data
+from torch.nn import init
+
+
+# 读取文本并分割成单词列表
+def get_text_tokenize(text_file):
+    with open('./sentence/' + text_file, 'r') as f:
+        output = f.read()
+    tokenizer_item = get_tokenizer("basic_english")
+    return tokenizer_item(output)
+
+
+class TextCNNDataProcessor:
+    def __init__(self, device, args):
+        super(TextCNNDataProcessor, self).__init__()
+
+        self.device = device  # 可用设备
+
+        self.batch_size = args.batch_size  # batch大小
+
+        self.static = args.static  # 静态加载词汇表
+        self.vocab_size = None  # 词汇表的长度
+        self.embedding_dim = args.embedding_dim  # 词的维度
+        self.label_num = args.label_num  # 标签个数
+
+        self.text_field = None  # 词汇表
+        self.label_field = None  # 标签表
+
+    # 加载数据
+    def load_data(self):
+
+        # 定义数据字段
+        """
+            如果需要设置文本的长度，则设置fix_length,否则torchtext自动将文本长度处理为最大样本长度
+            text = data.Field(sequential=True, tokenize=tokenizer, fix_length=args.max_len, stop_words=stop_words)
+        """
+        text_field = data.Field(sequential=True, tokenize=get_text_tokenize, lower=True)
+        label_field = data.Field(sequential=False, use_vocab=False)
+
+        # 读取数据并根据定义的字段加载数据
+        train, val, test = data.TabularDataset.splits(
+            path='./list', train='train.csv', validation='val.csv', test='test.csv', format='csv', skip_header=True,
+            fields=[('', None), ('index', None), ('image', None), ('text', text_field), ('label', label_field)]
+        )
+
+        # 加载并构建静态词向量表
+        if self.static:
+            text_field.build_vocab(train, val, test, vectors='glove.6B.300d')
+            text_field.vocab.vectors.unk_init = init.xavier_uniform
+        # 构建动态词向量，训练自动调整（此处不完整，缺少用户设置的embedding_dim）
+        else:
+            text_field.build_vocab(train, val, test)
+
+        # 构建标签的词汇表（可用于解决非数字标签的数据集）
+        # 标签build后：对于无标签<unk>为0，有标签0和1在build之后分别为1和2
+        # 后续使用标签：在后面训练模型时标签减1相对应
+        label_field.build_vocab(train, val, test)
+
+        # 构建batch大小的数据集
+        '''
+        torchtext.data.Iterators
+            Source: https://pytorch.org/text/stable/data.html
+            
+            (0) 默认shuffle在train=True的情况下为True
+            (1) 在使用sort_key时，必须设置 shuffle=False 且 sort=True
+            (2) 这里sort_key根据文本的长度进行排序
+                sort_key用于对示例进行排序的键，以便将具有相似长度的示例批量组合在一起并最小化填充
+            (3) lambda可以理解为箭头函数，输入x返回x的text属性值的长度
+            (4) 这里val验证集的batch_size则正好为验证集的长度，不用进行细分
+            (5) 这里device用于指定创建变量的设备（默认为cpu），如果使用gpu需要指定设备字符串
+            
+            实验说明：虽然这里定义了Iterator，但是正在实现Iterator的调用是在模型的训练过程，同时也是每个batch的过程
+        '''
+        train_iter = data.Iterator(train, batch_size=self.batch_size, shuffle=False, sort=True,
+                                   sort_key=lambda x: len(x.text), device=self.device)
+
+        val_iter = data.Iterator(val, batch_size=self.batch_size, shuffle=False, sort=True,
+                                 sort_key=lambda x: len(x.text), device=self.device)
+
+        test_iter = data.Iterator(test, batch_size=self.batch_size, train=False, sort=False, device=self.device)
+
+        self.vocab_size = len(text_field.vocab)  # 词向量表的长度
+        self.embedding_dim = text_field.vocab.vectors.size()[-1]  # 词向量维度
+        self.label_num = len(label_field.vocab)  # 标签个数，标签词汇表的长度
+
+        self.text_field = text_field
+        self.label_field = label_field
+
+        return train_iter, val_iter, test_iter
+
+    # 返回当前数据加载的参数：vocab_size、embedding_dim、label_num
+    def get_args(self):
+        return self.vocab_size, self.embedding_dim, self.label_num
+
+    # 返回当前构建的词汇表
+    def get_build_vocab(self):
+        return self.text_field.vocab.vectors
+
+    # 返回当前构建的标签表
+    # def get_label_vocab(self):
+    #     return self.label_field.vocab.vectors
diff --git a/textcnn_main.py b/textcnn_main.py
new file mode 100644
index 0000000..6bc48c6
--- /dev/null
+++ b/textcnn_main.py
@@ -0,0 +1,81 @@
+import torch
+import argparse
+
+from textcnn_model import TextCNN
+from textcnn_data_processer import TextCNNDataProcessor
+from textcnn_trainer import TextCNNTrainer
+from textcnn_data_feature import TextCNNDataFeature
+import random
+import numpy as np
+from torch.backends import cudnn
+
+
+def init_seeds(seed=0):
+    torch.manual_seed(seed)             # set the seed for generating random numbers
+    torch.cuda.manual_seed(seed)        # set the seed for generating random numbers for the current GPU
+    torch.cuda.manual_seed_all(seed)    # set the seed for generating random numbers on all GPUs
+    random.seed(seed)
+    np.random.seed(seed)
+
+    if seed == 0:                       # cudnn中对卷积操作进行了优化，牺牲了精度来换取计算效率
+        cudnn.deterministic = True      # 不过实际上这个设置对精度影响不大，仅仅是小数点后几位的差别
+        cudnn.benchmark = False         # 如果不是对精度要求极高，其实不太建议修改，因为会使计算效率降低
+
+
+init_seeds(1)
+
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+parser = argparse.ArgumentParser(description='TextCNN text classifier')
+
+parser.add_argument('-lr', type=float, default=0.001, help='学习率')
+parser.add_argument('-epoch', type=int, default=20, help='epoch，每一个epoch大小所有样本都训练了一次')
+parser.add_argument('-batch-size', type=int, default=50, help='batch，每一个batch大小更新一次参数')
+
+parser.add_argument('-filter-num', type=int, default=100, help='卷积核的个数')
+parser.add_argument('-filter-sizes', type=str, default='3,4,5', help='不同卷积核大小')
+parser.add_argument('-dropout', type=float, default=0.5, help='随机失活率')
+
+parser.add_argument('-static', type=bool, default=True, help='是否使用预训练词向量')
+parser.add_argument('-label-num', type=int, default=20, help='标签个数（可自动获取）')
+parser.add_argument('-embedding-dim', type=int, default=300, help='词向量的维度（预训练词向量可自动获取）')
+parser.add_argument('-fine-tune', type=bool, default=True, help='预训练词向量是否要微调，不需要微调设置为True')
+
+# parser.add_argument('-cuda', type=bool, default=True, help='是否使用GPU')
+parser.add_argument('-log-interval', type=int, default=1, help='经过多少iteration记录一次训练状态')
+parser.add_argument('-test-interval', type=int, default=50, help='经过多少iteration对验证集进行测试')
+parser.add_argument('-early-stopping', type=int, default=1000, help='早停时迭代的次数')
+parser.add_argument('-save-best', type=bool, default=True, help='当得到更好的准确度是否要保存')
+parser.add_argument('-model-save-dir', type=str, default='model_dir', help='存储训练模型位置')
+parser.add_argument('-feature-save-dir', type=str, default='feature_dir', help='存储文本TextCNN特征')
+
+args = parser.parse_args()
+
+
+if __name__ == '__main__':
+    print('torch.cuda.is_available(): ', torch.cuda.is_available())
+
+    print('1.正在加载数据...')
+    processor = TextCNNDataProcessor(DEVICE, args)
+    train_iter, val_iter, test_iter = processor.load_data()
+    args.vocab_size, args.embedding_dim, args.label_num = processor.get_args()
+    args.vectors = processor.get_build_vocab()
+    print('\n加载数据完成！\n')
+
+    print('2.正在加载模型...')
+    model = TextCNN(args).to(DEVICE)
+    print('\n加载模型完成！\n')
+
+    print('3.开始训练模型...')
+    trainer = TextCNNTrainer(args)
+    trainer.train(train_iter, val_iter, model)
+    print('\n训练模型完成！\n')
+
+    print('4.测试静态模型...')
+    trainer.test(model, test_iter)
+    print('\n测试模型完成！\n')
+
+    print('5.获取特征...')
+    feature_extractor = TextCNNDataFeature(model, args)
+    feature_extractor.save_features_labels(train_iter, val_iter, test_iter)
+    print('\n获取特征完成！\n')
diff --git a/textcnn_model.py b/textcnn_model.py
new file mode 100644
index 0000000..839392b
--- /dev/null
+++ b/textcnn_model.py
@@ -0,0 +1,61 @@
+import torch.nn as nn
+import torch.nn.functional as F
+import torch
+
+
+class TextCNN(nn.Module):
+    # 初始化
+    def __init__(self, args):
+        super(TextCNN, self).__init__()
+
+        self.args = args  # 所有参数（arguments）
+        filter_num = args.filter_num  # 卷积核的个数
+        filter_sizes = [int(fsz) for fsz in args.filter_sizes.split(',')]   # 卷积核数组，默认[3,4,5]
+
+        vocab_size = args.vocab_size        # 词汇表长度（根据数据集构建和确定）
+        embedding_dim = args.embedding_dim  # 词向量维度（根据具体使用的词向量的维度确定）
+        label_num = args.label_num          # 标签个数
+
+        self.embedding = nn.Embedding(vocab_size, embedding_dim)  # 词向量矩阵
+
+        if args.static:  # 静态词向量（如果使用预训练，词向量则提前加载，当不需要微调时设置freeze为True）
+            self.embedding = self.embedding.from_pretrained(args.vectors, freeze=not args.fine_tune)
+            # model.embedding.weight.data.copy_(args.vocab.vectors)
+
+            # 卷积网络层
+        self.conv = nn.ModuleList([nn.Conv2d(1, filter_num, (fsz, embedding_dim)) for fsz in filter_sizes])
+
+        self.dropout = nn.Dropout(args.dropout)  # dropout 随机丢弃，默认0.5
+
+        self.linear = nn.Linear(len(filter_sizes) * filter_num, label_num)  # 确定输出向量维度：len(filter_sizes) * filter_num
+
+    # 前馈网络
+    def forward(self, x):
+        # 输入维度为（batch_size, max_len）
+        # max_len可以通过torchtext设置或自动获取为训练样本的最大长度
+
+        # 经过embedding,x的维度为 (batch_size, max_len, embedding_dim)
+        x = self.embedding(x)
+
+        # 经过view函数x的维度变为 (batch_size, input_channel=1, w=max_len, h=embedding_dim)
+        x = x.view(x.size(0), 1, x.size(1), self.args.embedding_dim)
+
+        # 经过卷积运算,x中每个运算结果维度为(batch_size, out_channel, w=max_len, h=1)
+        x = [F.relu(conv(x)) for conv in self.conv]
+
+        # 经过最大池化层,维度变为(batch_size, out_channel, w=1, h=1)
+        x = [F.max_pool2d(input=x_item, kernel_size=(x_item.size(2), x_item.size(3))) for x_item in x]
+
+        # 将不同卷积核运算结果维度 (batch, out_channel, w=1, h=1) 展平为 (batch, out_channel * w * h)
+        x = [x_item.view(x_item.size(0), -1) for x_item in x]
+
+        # 将不同卷积核提取的特征组合起来,维度变为 (batch, sum:out_channel * w * h)
+        x = torch.cat(x, 1)
+
+        # dropout层，随机丢失部分值（设置为0）
+        x = self.dropout(x)
+
+        # 全连接层
+        logistic = self.linear(x)
+
+        return logistic
diff --git a/textcnn_trainer.py b/textcnn_trainer.py
new file mode 100644
index 0000000..e934cd3
--- /dev/null
+++ b/textcnn_trainer.py
@@ -0,0 +1,104 @@
+import torch
+import torch.nn.functional as F
+import os
+import sys
+
+
+class TextCNNTrainer:
+    def __init__(self, args):
+        super(TextCNNTrainer, self).__init__()
+        self.lr = args.lr
+        self.epoch = args.epoch
+        self.log_interval = args.log_interval
+        self.test_interval = args.test_interval
+        self.save_best = args.save_best
+        self.model_save_dir = args.model_save_dir
+        self.early_stopping = args.early_stopping
+
+    def train(self, train_iter, val_iter, model):
+        optimizer = torch.optim.Adam(model.parameters(), lr=self.lr)
+        steps = 0
+        best_acc = 0
+        last_step = 0
+        model.train()
+
+        for epoch in range(1, self.epoch + 1):
+            for batch in train_iter:
+                with torch.no_grad():
+                    feature, target = batch.text.t_(), batch.label
+
+                optimizer.zero_grad()
+                logits = model(feature)
+                loss = F.cross_entropy(logits, target)
+                loss.backward()
+                optimizer.step()
+
+                steps += 1
+
+                # 通过第一次batch的按顺序数据获取，并验证最后整个batch的数据存取顺序，再次验证：
+                # (1) 一个batch的数据就是一个分布式计算的最小单位
+                # (2) 一个batch在整个batch运行的顺序是不能确定的，就是不能用一个batch的数据顺序验证最后的结果
+                # (3) 以下代码是打印第一个batch的数据，用于比较最后的结果。已经没有用了
+                # if epoch == 1 and steps < 10:
+                #     index = steps - 1
+                #     print('\n\nsteps: ', steps, ' , label: ', target[index], ' ,feature: ', feature[index])
+                #     feature_list = []
+                #     for feature_item in feature[index]:
+                #         feature_list.append(batch.dataset.fields['text'].vocab.itos[feature_item])
+                #     print(feature_list, ' ', batch.dataset.fields['label'].vocab.itos[target[index]] + '\n\n')
+
+                if steps % self.log_interval == 0:
+                    # torch.max(logits, 1)函数：返回每一行中最大值的那个元素，且返回其索引（返回最大元素在这一行的列索引）
+                    corrects = (torch.max(logits, 1)[1] == target).sum()
+                    train_acc = 100.0 * corrects / batch.batch_size
+                    sys.stdout.write('\rBatch[{}] - loss: {:.6f}  acc: {:.4f}%({}/{})'
+                                     .format(steps, loss.item(), train_acc, corrects, batch.batch_size))
+
+                if steps % self.test_interval == 0:
+                    model.eval()
+                    dev_acc = self.validate(model, val_iter)
+                    if dev_acc > best_acc:
+                        best_acc = dev_acc
+                        last_step = steps
+                        if self.save_best:
+                            print('Saving best model, acc: {:.4f}%\n'.format(best_acc))
+                            self.save(model, self.model_save_dir, 'best', steps)
+                    else:
+                        if steps - last_step >= self.early_stopping:
+                            print('\nearly stop by {} steps, acc: {:.4f}%'.format(self.early_stopping, best_acc))
+                            raise KeyboardInterrupt
+                    model.train()
+
+    def validate(self, model, val_iter):
+        corrects, avg_loss = 0, 0
+        for batch in val_iter:
+            with torch.no_grad():
+                feature, target = batch.text.t_(), batch.label
+
+            logits = model(feature)
+            loss = F.cross_entropy(logits, target)
+            avg_loss += loss.item()
+            corrects += (torch.max(logits, 1)
+                         [1].view(target.size()) == target).sum()
+        size = len(val_iter.dataset)
+        avg_loss /= size
+        accuracy = 100.0 * corrects / size
+        print('\nEvaluation - loss: {:.6f}  acc: {:.4f}%({}/{}) \n'
+              .format(avg_loss, accuracy, corrects, size))
+        return accuracy
+
+    def save(self, model, save_dir, save_prefix, steps):
+        if not os.path.isdir(save_dir):
+            os.makedirs(save_dir)
+        save_prefix = os.path.join(save_dir, save_prefix)
+        # save procedure model
+        save_path = '{}_steps_{}.pt'.format(save_prefix, steps)
+        torch.save(model.state_dict(), save_path)
+        # save best model
+        save_best_path = '{}_steps.pt'.format(save_prefix)
+        torch.save(model.state_dict(), save_best_path)
+
+    def test(self, model, test_iter):
+        model.load_state_dict(torch.load('./{}/best_steps.pt'.format(self.model_save_dir)))
+        model.eval()
+        self.validate(model, test_iter)