Abstract: Traditional Chinese medicine (TCM) has thousands of years of experience in preventing diseases, while TCM constitution, as an important part of TCM, is closely related to individual health and thus plays an important role in disease prevention and treatment. In recent years, the rapid development of information technology and artificial intelligence has promoted the widespread application of numerous intelligent technologies in the field of TCM constitution recognition. These technologies not only make the traditional TCM constitution identification process more scientific and systematic, but also provide strong technical support for the modernization of TCM and personalized medicine, aiming to further improve the accuracy and efficiency of TCM constitution identification. In order to promote the research work on intelligent identification of TCM constitution, the research progress of its method is sorted out and summarized. Firstly, a systematic overview of the data analysis-based methods of constitution identification is made from the data level. Secondly, the traditional machine learning-based methods of constitution identification are reviewed and discussed, and compared from the perspective of classifiers. Lastly, the deep learning-based methods of constitution identification are elaborated and categorized into early neural networks, convolutional neural networks, hybrid networks, and other methods from the perspective of network architectures. For each of these three methods, they are analyzed according to their research methods and results, their advantages and limitations are compared, and the potential development trends in future research work are discussed.

Key words: traditional Chinese medicine constitution, body constitution identification, machine learning, deep learning

摘要： 中医在预防疾病方面有着数千年的经验，而中医体质则作为中医的重要组成部分，与个体健康密切相关，因此在疾病预防和治疗中发挥着重要作用。近年来，信息技术与人工智能的快速发展，推动了众多智能技术在中医体质辨识领域的广泛应用。这些技术不仅使传统的中医体质辨识过程更加科学系统化，还为中医的现代化和个性化医疗提供了强有力的技术支持，旨在进一步提高中医体质辨识的准确性和效率。为了推进中医体质智能辨识的研究工作，对其方法的研究进展进行了梳理与总结。从数据层面对基于数据分析的体质辨识方法进行了系统性概括；回顾并讨论了基于传统机器学习的体质辨识方法，从分类器的角度进行比较；阐述了基于深度学习的体质辨识方法，并从网络架构角度将其划分为早期神经网络、卷积神经网络、混合网络及其他方法。针对上述三种方法，根据其研究方法和结果分别进行了分析，比较了各自的优势与局限性，并讨论了未来研究工作中的潜在发展趋势。

关键词: 中医体质, 体质辨识, 机器学习, 深度学习