针对红花抗肿瘤多成分、多靶点导致机制解析难的问题，利用大数据技术突破中药复杂体系研究瓶颈。整合多组学与网络药理学，构建红花成分—肿瘤靶点关联库；结合分子对接、机器学习及GO/KEGG富集分析，绘制“成分—靶点—通路”调控网络。明确红花黄色素、黄酮类等为主效成分，显著抑制肝癌、肺癌、乳腺癌；靶向AKT1、TP53等，调控PI3K/Akt、MAPK通路，通过抑增殖、促凋亡、阻周期、抗转移发挥效应。大数据实现机制全景解析，为中药现代化与新制剂研发提供新范式。

Aiming at the difficulty in elucidating the anti-tumor mechanism of Carthami Flos arising from its multi component and multi-target characteristics, big data technology was adopted to break through the research bottlenecks in the complex system of traditional Chinese medicine (TCM). Multi-omics and network pharmacology were integrated to construct a correlation database of Carthami Flos components and tumor targets. Combined with molecular docking, machine learning, and GO/KEGG enrichment analysis, a regulatory network of “component-target-pathway” was mapped. It was confirmed that the main active components of Carthami Flos include safflower yellow pigments and f lavonoids, which exert significant inhibitory effects on liver cancer, lung cancer and breast cancer. These components target AKT1, TP53 and other molecules, regulate the PI3K/Akt and MAPK signaling pathways, and exert their anti-tumor effects by inhibiting cell proliferation, promoting apoptosis, blocking the cell cycle and suppressing metastasis. Big data enables a panoramic elucidation of the anti-tumor mechanism of Carthami Flos, providing a new paradigm for the modernization of TCM and the research and development of new TCM preparations.