[Objective] This paper builds a co-topics network to analyze the relationship among the topics of research articles and then optimize terms representing these topics. [Methods] First, we transformed the “document-topics” bipartite Graph to co-topics networks in accordance with weighted projection rules. Second, we identified the key topics with the combination of betweenness centrality and topic probability. Third, we divided the co-topics network community with the GN algorithm. Finally we optimized topic terms with relevance method. [Results] We compared the co-topics networks and the K-means based on JSD by testing optimal topic number (28) and random subjective topic numbers(20, 30). Their clustering numbers were the same and the consistent degree of clustering content reached 100%, 95% and 87%. [Limitations] We did not include other community partition methods with the proposed co-topics networks. [Conclusions] The co-topics network meets the demands of high-dimensional data and identifies the key topics and the closely linked topics of the target documents.
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