[Purpose/Significance] Literature analysis and social network analysis are important ways of knowledge discovery. Based on the dynamic and hierarchical features of knowledge evolution, this paper proposes a knowledge deconstruction and migration path identification method based on dynamic multilayer networks. [Method/Process] Firstly, the hierarchical network deconstruction was determined through equal time segmentation, knowledge element extraction, knowledge network construction and layering. Then, the knowledge migration span was calculated by the common node percentage in single-layer network and Jaccard coefficient, the importance of knowledge elements was determined by Simpson’s diversity index, and the main paths of knowledge migration were filtered. On this basis, the knowledge network was reconstructed to clarify the internal logic among the knowledge elements. In this paper, 22,049 papers from 24 journals in information management from 2001 to 2021, were divided into four stages with a three-year interval. CiteSpace software was used for keyword extraction and preliminary statistics, and Pajek software for network stratification and reconstruction on the important paths. [Result/Conclusion] The results show that, in recent years, the information management field has been characterized by subfields centered on the enterprise, e-commerce and user behavior, and information technology and innovation, with information technology being an important connection between the enterprise and the user. Comparing with cluster analysis and time expanded network method, the proposed approach can not only better demonstrate the importance of knowledge elements, but also the reconstructed network shows a more specific and clear logic of knowledge element modules and their internal relationships.
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