The mechanism of Curcumae Rhizoma-Trionycis Carapax drug pair (CRTC) in treating liver fibrosis was explored by using network pharmacology,molecular docking technology and experiment validation.Firstly,we collected the chemical components and targets information of Curcumae Rhizoma (CR) and Trionycis Carapax (TC);then merged the targets of the two drugs and removed duplicates.We used diverse disease databases to obtain genes information related to liver fibrosis.And we extracted the intersection targets of CRTC and liver fibrosis,further visualized the network of “drugs-potential active ingredients-potential targets”.We imported intersection targets into the STRING database to construct a protein-protein interaction (PPI) network;performed GO function and KEGG pathway enrichment analysis and visualization,and then used AutodockVina software to construct molecular docking models.Finally,the targets and pathways predicted by network pharmacology were experimentally validated through in vivo experiments.Totally,65 intersection targets between CRTC and liver fibrosis were identified.In PPI network,the top 4 with the highest node connection values are interleukin-6 (IL-6),Akt serine/threonine kinase 1(AKT1),signal transducer and activator of transcription 3(STAT3) and peroxisome proliferative activated receptor gamma(PPARG),respectively.GO functional enrichment analysis involved 1 025 biological processes,41 cell components,and 84 molecular functions.KEGG pathway enrichment analysis identified 149 pathways,including key pathways such as EGFR tyrosine kinase inhibitor resistance.Molecular docking results showed that IL-6,STAT3 and other core targets had good binding activity with their corresponding components.The in vivo animal experimental results confirmed that CRTC can improve the pathological morphology of liver fibrosis and significantly inhibit the expression of IL-6,EGFR,and STAT3.In conclusion,CRTC acts against liver fibrosis through multiple targets and multiple pathways,its mechanism is related to the IL-6/EGFR/STAT axis predicted by network pharmacology and molecular docking.