隨著信息技術(shù)的快速發(fā)展及數(shù)字化時(shí)代的到來，數(shù)字化技術(shù)在多個(gè)中醫(yī)藥研究領(lǐng)域，如中藥質(zhì)量控制、數(shù)據(jù)挖掘、新藥發(fā)現(xiàn)、處方配伍優(yōu)化以及中醫(yī)臨床診斷等方面發(fā)揮重要作用。機(jī)器學(xué)習(xí)（ML）、深度學(xué)習(xí)（DL）等數(shù)字化技術(shù)的應(yīng)用可優(yōu)化中醫(yī)藥研究設(shè)計(jì)、降低臨床研究時(shí)間成本、將臨床研究與基礎(chǔ)研究有機(jī)結(jié)合，提高臨床研究質(zhì)量和效率，為中醫(yī)藥現(xiàn)代化研究的科學(xué)性、客觀性、規(guī)范性提供了保障。因此，完善多種數(shù)據(jù)資源、實(shí)現(xiàn)多種數(shù)字化技術(shù)交叉結(jié)合使用、優(yōu)化算法和模型是中醫(yī)藥未來發(fā)展的必然趨勢(shì)。對(duì)近年來ML在中醫(yī)藥研究領(lǐng)域中的應(yīng)用進(jìn)展進(jìn)行梳理，分析說明聚類、支持向量機(jī)（SVM）和深度學(xué)習(xí)（DL）等ML方法在中藥藥性、中藥配伍、中藥毒性分析、中藥藥效研究、中藥制藥過程工藝優(yōu)化、中藥飲片質(zhì)量等級(jí)分類、中醫(yī)藥臨床用藥規(guī)律挖掘、中醫(yī)藥治療原理及藥效機(jī)制解析等研究中的具體應(yīng)用。旨在探索ML在中醫(yī)藥研究中的應(yīng)用趨勢(shì)，并對(duì)其應(yīng)用前景進(jìn)行展望。

With the rapid development of information technology and the arrival of the digital era, digital technology plays an important role in several Chinese medicine research areas, such as quality control of traditional Chinese medicine (TCM), data mining, discovery of new medicines, optimization of prescription compounding, and clinical diagnosis of Chinese medicine. The application of digital technologies such as machine learning (ML) and deep learning (DL) can optimize the design of TCM research, reduce the time and cost of clinical research, organically combine clinical research with basic research, improve the quality and efficiency of clinical research, and provide a guarantee for the scientificity, objectivity, and standardization of modernized TCM research. Therefore, it is an inevitable trend for the future development of TCM to improve multiple data resources, realize the crosscombined use of multiple digital technologies, and optimize algorithms and models. In recent years, the progress of ML application in the field of TCM research is sorted out, and the analysis illustrates the specific applications of ML methods such as clustering, support vector machine (SVM) and deep learning (DL) in the research of TCM medicinal properties, TCM compounding, TCM toxicity analysis, TCM efficacy research, TCM pharmaceutical process optimization, TCM tablets quality grade classification, mining of TCM clinical medication rules, and analysis of TCM therapeutic principles and pharmacological effect mechanisms. The research aims to explore the application of ML in the study of TCM. It aims to explore the trend of ML application in TCM research and to prospect its application prospect.

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