The controllability and scientific rigor of traditional Chinese medicine (TCM) quality standards are fundamental to ensuring their clinical efficacy. The core limitation of current TCM quality standards lies in the inherent conflict between holistic and reductionist approaches, which hinders their international recognition under reductionist frameworks and significantly impedes TCM’s globalization. As a mathematical tool for processing complex high-dimensional data, tensor theory demonstrates exceptional computational efficiency when applied in artificial intelligence (AI) and big data technologies, thereby accelerating the development of sophisticated AI models and applications. Furthermore, tensor-based theoretical and algorithmic research in high-dimensional data modeling provides essential mathematical tools for practical problem-solving. By applying the second-order tensor correction method and the third-order tensor correction method, we can accurately analyze the components of TCM, thus establishing the logical relationship between the macro-quality of TCM and the micro-components, and effectively solving the contradiction between the integrity and reducibility of TCM quality. Therefore, tensor technology is expected to provide new theoretical support and technical means for the modernization of quality standards of TCM.

tensor  /  holism and reductionism  /  macroscopic pharmacological effects  /  microscopic components  /  quality standards