[Objective] This study aims to identify relationship among authors of papers with similar contents but different keywords, and then tries to add more sematic factors to the co-occurrence analysis. [Methods] We proposed a method to gauge the similarity of research interests based on the keywords semantic network system. First, all keywords were represented as word vectors and translated into low dismension distribution with the help of neural network language—word2vec model. Second, we calculated the semantic association of keywords to build up a semantic network. Finally, we adopted the Jensen-Shannon distance method to measure the similarity of research interests. [Results] The proposed approach can accurately identify the similarities of co-occurrence and non co-occurrence terms and then effectively predict potential cooperation among authors. [Limitations] The amount and accuracy of training materials need to be increased. At present, we could only find potential cooperation between two authors. More research is needed to explore the possibilities of cooperation among multi-authors. [Conclusions] The proposed method could help to improve the performance of traditional co-occurrence analysis.
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