中药质量控制方法的科学性是制约中药全球高质展发展的关键因素，同时在现有中药质量标准控制下的中药质量优劣也是中医药治疗疾病效果的关键因素。在人工智能和大数据快速发展的背景下，深度学习在中药质量控制研究中获得了广泛关注，相较于传统方法其展现出明显优势，并在中药外观识别、成分分析、安全性评估及工艺质量控制等方面取得了显著成果。从深度学习技术的基本框架和常见模型出发，系统梳理了深度学习在中药质量控制中的应用模式，深入分析了其在中药质量检验中面临的挑战，并基于已有研究成果提出了针对性的解决措施。总结了深度学习在中药质量控制领域的应用差距及未来发展趋势。以期为中药质量控制体系的升级和现代化发展提供新的思路。

The scientific nature of traditional Chinese medicine (TCM) quality control methods is a key factor that restricts the development of global high-quality exhibition of TCM, and at the same time, the quality of TCM under the existing quality control is also a key factor in the effectiveness of TCM in treating diseases. Under the background of rapid development of artificial intelligence and big data, deep learning has gained wide attention in the research of quality control of TCM, which shows obvious advantages compared with traditional methods and has achieved remarkable results in the recognition of the appearance of TCM, composition analysis, safety assessment and process quality control. Therefore, starting from the basic framework and common models of deep learning technology, this paper systematically combs through the application modes of deep learning in the quality control of TCM, deeply analyzes the challenges it faces in the quality inspection of TCM, and puts forward targeted measures to solve the problems based on the existing research results. Finally, the application gaps and future development trends of deep learning in the field of quality control of TCM are summarized. In order to provide new ideas for the upgrading and modernization of the quality control system of TCM.

TP18；R282.6

国家自然科学基金项目（81874344）；湖南省重点领域研发计划（2023SK2046）；湖南省卫生健康高层次人才重大科研专项（R2023139）；湖南省自然科学基金项目（2023JJ60474）；长沙市自然科学基金项目（kq2208148，kq2208191）；湖南创新型省份建设专项（2024RC8110）