Abstract: The Internet short-text classification is a hot research topic in natural language processing. This paper presents a short text classification method based on deep learning’s convolutional neural networks. First short-text features are achieved by the Skip-gram model of Word2vec, then it is sent into the CNNs to extract high-level features, after the K-max pooling, it is put into the Softmax classifier to get a classification model. In the Internet short-text classification experiments, compared to machine learning and DBN’s method, the results show that the proposed method not only solves the problems of the curse of dimensionality of text vector and the local optimal solution, but also effectively improves the accuracy of Internet short-text classification, and confirms the validity of the Internet short-text classification method based on CNNs.
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Key words:  CNNs, short-text classification, deep learning, machine learning