Text classification is one of the contents of text mining, which has a wide range of applications in the fields of information retrieval, e-mail filtering, web page classification and so on. At present, the text classification algorithm on the feature representation is still insufficient. This paper proposes a text classification algorithm based on a variety of features. In the algorithm. firstly, the word vector was obtained by using the skip-gram model on the segmentation of text. And then various pool methods are applied to get the vector of the entire text. Finally, the various pool features are a whole input, which is the input of the softmax regression model to obtain the categorization. Through the text classification corpus provided by Fudan University (Fudan) experimental test corpus, the results show that the proposed method can improve the accuracy of text classification, which shows the effectiveness of the proposed algorithm.
