Background：Peri-Implantitis (PI) is a pathological condition associated with dental plaque that occurs in the tissue around dental implant. The traditional methods of evaluating PI have limitations in clinical practice. Non-invasive and accurate diagnosis of PI is essential for determining the appropriate treatment. By integrating and analysing two microarray platform datasets from the GEO database, we aim to identify the diagnostic biomarkers of PI, find the target drugs , and guide the clinical diagnosis and treatment of PI. Methods: The differentially expressed genes (DEG) of PI were identified by integrating two datasets (GSE57631 and GSE106090) into the GEO database using sva and limma packages of R. Then, the main biological functions of DEG were analyzed by Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and Gene Set Enrichment Analysis (GSEA). The hub genes associated with PI were identified by WGCNA, and the protein-protein interaction (PPI) network was constructed by Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and Cytoscape. The core genes associated with PI were determined through LASSO regression . Meanwhile, The content of immune cells in normal tissue and PI were evaluated using the CIBERSORT . Finally, target drugs and the target miRNA of PI chromosomes were found by Drug Signature Database and TargetScan respectively. Results：A total of 425 DEG were found in PI, including 214 upregulated and 211 downregulated genes. GO analysis shows that DEGs were enriched in biological processes related to inflammation, the enriched pathways in the KEGG pathway analysis were Cytokine-cytokine receptor interaction, Chemokine signaling pathway, B cell receptor signaling pathway.The results of GSEA revealed that the gene expression of B cells was higher than myeloid (NES = 2.51) in Peri-Implantitis group. Ten hub genes associated with PI were found by WGCNA analysis. Two core genes (CD38 and IRF4) related to Peri-Implantitis were obtained by LASSO. 32 hub genes related to PI chromosomes were found by PPI network constructed by Cytoscape. 22 kinds of immune cells and 13 kinds of immune reaction processes related to PI were identified by CIBERSORT. Ten target drugs and ten target miRNA were found by Drug Signature Database and TargetScan respectively. Conclusions: Through the bioinformatics analysis of merged datasets, the Hub genes, immune cells and target drugs related to Peri-Implantitis chromosomes were screened. The results can provide guidance for the clinical treatment of Peri-Implantitis.