In view of the problem that the existing multi-document extraction method cannot make good use of sentence topic information and semantic information, this paper proposes a multi-document summarization extraction method that integrates multi-information sentence graph model. Firstly, a sentence graph model with sentences as nodes is constructed. Secondly, the Bayesian topic model based on sentences and the word vector model are combined to get the probability distribution of sentence topic and the semantic similarity of sentences, and the final relevance of sentences is obtained. The topic information and semantic information are used as the edge weights of the sentence graph model. Finally, the summary of the multi-document is described by the summary method of the minimum dominance set of the sentence graph. This method combines the topic information, semantic information and relationship information between sentences by integrating the multi-information sentence graph model. The experimental results show that the method can effectively improve the comprehensive performance of the summarization extraction.