[Purpose/significance] The research on topic identification of scientific literature is one of the important contents of scientific research management. How to comprehensively grasp the multivariate data of literature and effec? tively improve the accuracy of automatic literature topic identification is a problem worthy of research. [Method/pro? cess] Keywords and abstracts of documents are important basis for judging document topics. This paper proposes a top? ic identification model based on multi-data fusion of documents. Word2vec model, AP clustering and Node2vec model are used to represent the topic vector of the keyword layer. The topic vector of the abstract layer is represented by the LDA model, and the SGF method in the multi-view clustering method is used to perform data fusion and extract docu? ment topics. [Result/conclusion] Taking document sets of different scales as an example, through topic identification research, it is verified that the accuracy and interpretability of the recognition effect of the model are better than the typ? ical LDA method and the Doc-LDA model.