目的 基于文献计量学软件Citespace和VOSviewer对国内外郁金研究的热点领域及发展趋势进行可视化分析，为郁金的深入研究与应用提供参考。方法 以中国知网（CNKI）、万方数据知识服务平台（Wanfang）、Web of Science（WOS）、PubMed核心数据库作为数据资源，检索时间设定为2016年1月1日—2025年12月12日，采用CiteSpace 6.4 R1和VOSviewer 1.6.20软件对筛选所得文献进行发文量、作者、关键词分析。结果 纳入中文文献1 354篇，英文文献77篇，中文文献核心作者为史正刚（21篇），英文文献核心作者为Ji De（6篇）；中文研究形成以“数据挖掘”和“网络药理学”为代表的“临床经验-计算机制”双向知识网络；英文研究则聚焦“Curcumae Radix”与“molecular docking”，呈现出“计算预测-疾病聚焦”的纵深路径。研究热点从早期的临床疗效观察正快速向数据驱动与计算药理学机制解析转型。结论 郁金研究已从单一的药效观察演进为“临床问题”与“机制解析”，并重且深度整合计算模拟与系统生物学方法的系统性探索。未来应着力构建“计算预测-实验验证”的闭环研究体系，推进高质量循证转化，从而驱动该领域向精准医学与现代化药学方向转型。

Objective To conduct a visual analysis of hotspots and development trends in Curcumae Radix research domestically and internationally using bibliometric software CiteSpace and VOSviewer, and provide a reference for in-depth research and application of Curcumae Radix. Methods The China National Knowledge Infrastructure (CNKI), Wanfang Data Knowledge Platform, Web of Science (WOS) Core Collection, and PubMed databases were searched as data sources. The search period was set from January 1, 2016 to December 12, 2025. Analyses of publication volume, authors, and keywords were performed on the selected literature using CiteSpace6.4.R1 and VOSviewer1.6.20 softwares. Results A total of 1 354 Chinese literature and 77 English literature were included. Shi Zhenggang was identified as the core author of Chinese publications with 21 papers, while Ji De was the core author of English publications with six papers. Chinese research has formed a bidirectional knowledge network of “clinical experience-computational mechanism” represented by “data mining” and “network pharmacology”. English research focuses on “Curcumae Radix” and “molecular docking”, demonstrating an in-depth path of “computational prediction-disease focus”. The research hotspots are rapidly evolving from early-stage clinical efficacy observation to data-driven and computational pharmacological mechanistic elucidation. Conclusion Research on Curcumae Radix has evolved from simple efficacy observation into a systematic exploration that equally emphasizes “clinical problems” and “mechanistic elucidation”, while deeply integrating computational simulation and systems biology methods. Future efforts should focus on establishing a closed-loop research system of “computational prediction-experimental verification”, promoting high-quality evidence-based transformation, thereby driving the transformation and upgrading of this field toward precision medicine and modernized pharmacology.

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