近30年中医药领域文献计量学研究趋势可视化分析OA

Objective To systematically review and assess the fundamental characteristics of the relevant research literature in the field of traditional Chinese medicine,and to identify research hotspots and frontier topics,using knowledge mapping tools such as CiteSpace.Methods Based on the China National Knowledge Infrastructure(CNKI)database,a total of 2891 bibliometric articles related to traditional Chinese medicine published from 1996 to 2025 were retrieved.CiteSpace and Python were employed to conduct a multi-dimensional visual analysis of annual publication volume,institutions,authors,journals,and keywords.Results The annual publication volume in this field showed an overall upward trend,with a marked increase observed since 2019.A stable core author collaboration network had not yet been established,with Wang Hongwu,SUN Xuan,and WANG Dongjun identified as representative high-yield authors.Beijing University of Chinese Medicine,Hunan University of Chinese Medicine,and Tianjin University of Traditional Chinese Medicine were the primary high-yield institutions.High-yield journals were concentrated in World Science and Technology-Modernization of Traditional Chinese Medicine,Chinese Journal of Library and Information Science for Traditional Chinese Medicine,and Chinese Journal of Information on Traditional Chinese Medicine.High-frequency keywords included"Traditional Chinese Medicine""knowledge mapping""research hotspots""acupuncture""visualization"and"CiteSpace".Keyword clustering and burst detection analyses indicated that research hotspots had gradually shifted from traditional journal evaluation to the integration of data mining and knowledge mapping,with increasing emphasis placed on the application of visualization methods in Traditional Chinese Medicine topics.Conclusion Research on bibliometrics in the field of traditional Chinese medicine has been steadily increasing;however,institutions and authors remain widely dispersed,and collaboration networks require further strengthening.In the future,the deep integration of data mining,knowledge graphs,and visualization techniques is expected to drive the systematic and innovative development of this field.