As a model for solving clinical challenges, renowned medical cases have affirmed the correctness of diagnostic and treatment approaches as well as the effectiveness of practical outcomes through long-term clinical practice. However, traditional statistical methods struggle to comprehensively and deeply unveil the speculative sys-tem and empirical logic of traditional Chinese medicine (TCM), especially when confronted with its nonlinear, multi-dimensional, and complex relationships. In contrast, machine learning methods demonstrate significant advantages in addressing such issues and have been widely applied in the study and inheritance of TCM. This article aims to discuss how to use machine learning algorithms to study TCM medical cases, and to describe the acquisition and processing of TCM medical case data and the selection of machine learning algorithms, with a view to providing references for the study of TCM medical cases.

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