Abstract:The Chinese Weibo is an indispensable communication tool for people today. Mining information in Weibo text is of great significance to automatic question and answer, public opinion analysis and other applied research. The short text classification study is the basis of short text mining. The neural network-based Word2Vec can solve problems of high-dimensional sparseness and semantic gap that traditional text categorization methods cannot solve. This study obtains the word vector based on Word2Vec, then the class factor is introduced into the traditional weight calculation method TF-IDF (Term Frequency-Inverse Document Frequency) to design the word vector weight. Finally, the SVM classifier is used for classification. The effectiveness of the method is verified by experiments on Weibo data.