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Objective The purpose of this study was to investigate the specific alterations in gut microbiome and serum metabolome and their interactions in patients with polycystic ovary syndrome (PCOS). Methods The stool samples from 32 PCOS patients and 18 healthy controls underwent the intestinal microbiome analysis using shotgun metagenomics sequencing approach. Serum metabolome was analyzed by ultrahigh performance liquid chromatography quadrupole time-of-flight mass spectrometry. An integrative network by combining metagenomics and metabolomics datasets was constructed to explore the possible interactions between gut microbiota and circulating metabolites in PCOS, which was further assessed by fecal microbiota transplantation (FMT) in a rat trial. Results Fecal metagenomics identified 64 microbial strains significantly differing between PCOS and healthy subjects, half of which were enriched in patients. These changed species showed an ability to perturb host metabolic homeostasis (including insulin resistance and fatty acid metabolism) and inflammatory levels (such as PI3K/Akt/mTOR signaling pathways) by expressing sterol regulatory element-binding transcription factor-1, serine/threonine-protein kinase mTOR, and 3-oxoacyl-[acyl-cattier-protein] synthase III, possibly suggesting the potential mechanisms of gut microbiota underlying PCOS. By integrating multi-omics datasets, the panel comprising seven strains (Achromobacter xylosoxidans, Pseudomonas sp. M1, Aquitalea pelogenes, Porphyrobacter sp. HL-46, Vibrio fortis, Leisingera sp. ANG-Vp, and Sinorhizobium meliloti) and three metabolites [ganglioside GM3 (d18:0/16:0), ceramide (d16:2/22:0), and 3Z,6Z,9Z-pentacosatriene] showed the highest predictivity of PCOS (AUC: 1.0) with sensitivity of 0.97 and specificity of 1.0. Moreover, the intestinal microbiome modifications by FMT were demonstrated to regulate PCOS phenotypes including metabolic variables and reproductive hormones. Conclusion Our findings revealed key microbial and metabolite features and their interactions underlying PCOS by integrating multi-omics approaches, which may provide novel insights into discovering clinical diagnostic biomarkers and developing efficient therapeutic strategies for PCOS.