[Objective] This paper aims to correctly grasp the topic development trend by constructing a microblog topic evolution method, and it is of great significance for public sentiment warning. [Methods] Firstly, the Ship-gram model is used to train the word vector model on the text set. Input the text of each time slice into the BTM to get the candidate theme. In BTM thematic dimension, the theme word vector is constructed. Secondly, k-means algorithm is used to cluster the theme word vector to get the fused theme. And the topic evolution of the text set on time slice is established. [Results] The experimental results show that the F value of this method is 75%, which is about 10% higher than that of the topic model. This proves the feasibility of the proposed method. [Limitations] There is no definite measuring standard for topic evolution, and there is no comparison between various methods of topic evolution. [Conclusions] The proposed method can effectively extract topics at all stages and provide an effective way for network public opinion analysis.
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