Abstract: As the fast development of social network, instant messaging system has become an essential communication tool in our daily lives. We can quickly exchange information about life, technology and work through online group chat. However, due to the faster update of group chat messages, it is difficult for us to obtain group chat topics. And traditional topic mining models are not well suited to the topic mining of group chat texts. By analyzing the characteristics of group chat messages, GRU and LDA Based Group Chat Topic Mining(GLB-GCTM) model is proposed, which solves the problem of word order that cannot be solved by traditional theme models. First, assuming that each document has a Gaussian-distribution topic vector, then the latent state of each word is generated according to the GRU, and the current word is determined as a stop word based on the Bernoulli distribution of the latent state of the current word to determine which language model to use. This method uses ten QQ groups that authors join in and collect the last three-months group chat messages for test. The model can effectively identify the topics in the group chat text combined with the comparative experiment evaluation criteria.

Key words: topic mining, group chat, deep learning, GRU； LDA