中文摘要：

文本分类中的高维数据和噪声一直是影响文本分类准确率的主要因素,特征选择和特征提取是降维和去噪的主要手段.本文提出根据词的类间概率分布方差和文档分布方差改进TF-IDF的特征选择方法（VAR-TF-IDF）,调整Word2vec中的CBOW＋HS词向量训练框架,用特征词词向量的叠加作为文本的特征向量,有效地提高了文本分类的准确率和召回率.实验算例证明了所提方案的有效性.

英文摘要：

High dimensional data and noise have always been the major factors affecting the accuracy of text classification. Feature selection and feature extraction is the main methods of dimensionality reduction and denoising. In this paper, the words probability distribution variance and document distribution variance is used to improve the TF-IDF feature selection method（VAR-TF-IDF）. After selecting good features, it tuned the CBOW＋HS frame work of word2 vec. The superposition of word embedding of the selected words is used as eigenvector which could improve accuracy of text classification. Experiment shows the proposed method is effective.