Abstract: Objective: To comprehensively and systematically elucidate the role of the regulatory network of gutassociated microbiota metabolites(GMMs) on the onset of rheumatoid arthritis(RA) while identifying important molecular targets and choosing potential drug molecules. Methods: The differentially expressed genes derived from the RA gene chip data were combined with the genes from the weighted gene co-expression network analysis module, utilizing databases such as SwissTargetPrediction(STP), Similarity Ensemble Approach(SEA), and GutMgene to derive the target gene set associated with gut microbiota metabolites. After that, the intersection between the above data with RA-associated genes obtained from GeneCards, OMIM and CTD was identified. GO/KEGG functional annotation and topological evaluation of protein-protein interaction networks were used to filter key targets. A multi-dimensional regulatory framework of "micro-organism-metabolite-gene target" was established, and drug characteristics and toxicity profiles were assessed using SwissADME and ADMETlab platforms. Molecular docking studies were carried out to validate the interaction of the selected lead compounds and core targets. At the same time, using a RA cell model, the in vitro activity of the candidate drugs was evaluated in synovial fibroblasts through half-maximal inhibitory concentration(IC50), cell counting kit-8(CCK-8) proliferation assay, clonogenic assay and other methods. Results: A total of 40 common genes were successfully identified through multi-dimensional analysis, in which IL6, TNF, IL1β, AKT1 and TP53 were confirmed to be key regulatory factors. The pathway enrichment results indicated that these genes were significantly involved in everal critical pathological processes of RA, including PI3K-Akt, MAPK, TNF signaling transduction, Th17 cell differentiation and NOD-like receptor pathways. An interaction network containing 154 bacterial metabolites, 1, 518 metabolism-related targets and 1, 933 disease-related targets was constructed, which highlighted the regulatory role of Flavonifractor plautii and Blautia sp. in modulating core targets. These microbiota regulated the action mode of the core target via tryptophan and tyrosine metabolic pathways. The assessment of efficacy and toxicity led to the identification of lead compounds with good drugability, which exhibited a significant binding activity to targets like IL6 and TNF. This compound had an IC50 value of 40.85 μmol/L for synovial fibroblasts based on in vitro studies and could inhibit cell proliferation and colony formation in a dose-dependent manner. When the concentration was no more than 80 μmol/L, the survival rate of cells was over 80%, showing excellent safety characteristics. Conclusion: This study systematically elucidates the key mechanism by which the gut microbiota regulates the immune-inflammatory signaling axis in the pathogenesis of RA through its metabolites. The identified core gene network and the candidate drug 3-(4-Hydroxyphenyl)propionic acid provide a scientific basis for precision treatment strategies of RA and open up new avenues for drug repurposing research.