[目的/意义]科学文献的主题识别研究是科研管理的重要内容之一，如何全面把握文献的多元数据、提升 自动文献主题识别的效果是一个值得研究的问题。[方法/过程]文献的关键词、摘要是判断文献主题的重要依据， 文章提出基于文献多元数据融合的主题识别模型，使用Word2vec模型、AP聚类及Node2vec模型表示出关键词层 的主题向量，使用LDA模型表示出摘要层的主题向量，通过多视图聚类中的SGF方法进行数据融合并识别文献 主题。[结果/结论]以不同规模的文献集为例，通过主题识别研究，验证该模型识别效果的准确性和可解释性优于 典型LDA方法、Doc-LDA模型。

[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.