摘要Network pharmacology has gained widespread application in drug discovery,particularly in traditional Chinese medicine(TCM)research,which is characterized by its"multi-component,multi-target,and multi-pathway"nature.Through the integration of network biology,TCM network pharmacology enables systematic evaluation of therapeutic efficacy and detailed elu-cidation of action mechanisms,establishing a novel research paradigm for TCM moderniza-tion.The rapid advancement of machine learning,particularly revolutionary deep learning methods,has substantially enhanced artificial intelligence(AI)technology,offering signific-ant potential to advance TCM network pharmacology research.This paper describes the methodology of TCM network pharmacology,encompassing ingredient identification,net-work construction,network analysis,and experimental validation.Furthermore,it summar-izes key strategies for constructing various networks and analyzing constructed networks us-ing AI methods.Finally,it addresses challenges and future directions regarding cell-cell com-munication(CCC)-based network construction,analysis,and validation,providing valuable insights for TCM network pharmacology.