中药质量控制是保障中药安全、有效、稳定与可控的核心环节。随着“中药质量标志物（Q-Marker）”理论的提出，以及生物分析、组学技术与人工智能（artificial intelligence，AI）的快速发展，中药质量控制正经历从“经验＋科学”向“科学＋智能”的跃升。Q-Marker理论为中药质量控制从“单指标成分”向“多维度、系统化、临床导向”的综合评价体系转型提供了理论支撑。系统梳理了近10年的相关研究进展，重点分析了AI在近红外光谱（near-infrared spectroscopy，NIR）快检、质量标志物发现、多源数据融合建模及全链条智能监管中的关键作用。AI通过自动特征提取、多模态融合、深度学习建模与可释性分析，能显著提升复杂中药体系中“成分-药效-临床”相关联的解析能力，推动NIR在线检测技术的智能化升级。基于此提出了构建以Q-Marker为核心、AI为驱动、数据为纽带的中药质量数智评价体系，为实现“成分清晰、效应可测、过程可视、信息共享”的智能监管范式提供了借鉴，为中药质量控制迈向标准化、国际化和高质量发展提供了参考路径。

Quality control is a core link in ensuring the safety, efficacy, consistency, and controllability of traditional Chinese medicine (TCM). Over the past decade, with the proposal of the “quality marker (Q-Marker)” concept and the rapid advancement of bioanalytical techniques, omics technologies, and artificial intelligence (AI), TCM quality control is undergoing a transition from an “experience-plus-science” paradigm to a “science-plus-intelligence” paradigm. The Q-Marker theory provides theoretical support for the transformation of TCM quality control from a “single-index component” approach to a multi-dimensional, systematic, and clinically oriented comprehensive evaluation system. This paper systematically reviews the research progress over the last decade, with a focus on analyzing the key roles of AI in near-infrared (NIR) spectroscopy rapid detection, Q-Marker discovery, multi-source data fusion modeling, and whole-chain intelligent regulation. The research demonstrates that AI, through automated feature extraction, multimodal fusion, deep learning-based modeling, and interpretability analysis, significantly enhances the ability to decode the “component-efficacy-clinical outcome” relationships within complex TCM systems, thereby driving the intelligent upgrading of online analytical technologies such as NIR. On this basis, we propose a digital and intelligent TCM quality evaluation system with Q-Markers as the core, AI as the driving force, and data as the bridge. This system achieves an intelligent regulatory paradigm characterized by “clear components, measurable efficacy, visible processes, and shared information”, and represents a key pathway for advancing TCM quality control toward standardization, internationalization, and high-quality development.

R28;TP18

“带土移植”人才引育计划项目（桂科AA23026008）