目的 归纳总结中药质量标志物（quality marker，Q-Marker）研究现状及预测其发展趋势。方法 利用中国知网（CNKI）和Web of Science核心集数据库，检索中药Q-Marker的相关文献，运用文献计量学及CiteSpace 5.7.R5软件对研究作者、研究机构及关键词等内容进行可视化展示。结果 经过筛选最终入选中文文献279篇、英文文献70篇。中、英文文献作者合作网络分析显示，刘昌孝是Q-Marker研究领域发文量最多的作者，并能形成稳定的核心研究团队；发文机构分析显示，天津药物研究院和天津中医药大学是Q-Marker研究的重要科研机构，机构间合作较多；中、英文文献关键词分析显示，Q-Marker的研究内容主要集中在质量标准、指纹图谱、质量控制、一测多评、网络药理学等相关领域。结论 通过网络药理学、代谢组学、化学物质组学结合现代分析技术辨识中药及复方、经典名方的Q-Marker，并用于质量控制是当前研究热点。中药Q-Marker的研究处于高速发展时期，未来在蛋白、基因和信号通路层面辨识和验证Q-Marker是发展趋势。

Objective To summarize the research status and predict the development trend of quality marker (Q-marker) in traditional Chinese medicine. Methods CNKI and Web of Science core database were used to search the related literatures of Q-Marker. Bibliometrics and CiteSpace 5.7.R5 software were used to visualize the research authors, research institutions, and keywords. Results After screening, a total of 279 Chinese and 70 English literatures were selected. According to the analysis of the collaboration network of Chinese and English literature authors, Chang-xiao Liu was the most prolific author in Q-Marker research field, who can form a stable core research team. The analysis of the issuing institutions showed that Tianjin Institute of Pharmaceutical Research and Tianjin University of Traditional Chinese Medicine were important scientific research institutions of Q-Marker research, with more cooperation among them. The keywords analysis of Chinese and English literature showed that the research related to Q-Marker mainly focused on quality standard, fingerprint, quality control, one test multiple evaluation, network pharmacology, and other related fields. Conclusion It is a hot topic to identify the Q-Marker of traditional Chinese medicine, compound prescription and classic prescription through network pharmacology, metabonomics, chemical matter omics combined with modern analytical technology, and applied it to quality control. The research on Q-Marker of traditional Chinese was in a period of rapid development. In the future, it will be the development trend to identify and verify Q-Marker at protein, gene and signaling pathway scale.

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中国科学院科技服务网络计划（STS计划）区域重点项目（KFJ-STS-QYZD-2021-03-002）；广西自然科学基金项目（2020GXNSFAA238035）；广西科技基地和人才专项（桂科AD20238058）