Objective: To identify immune-related feature genes in ovarian cancer through bioinformatics analysis and perform immune-related investigations, which hold significant value for the early diagnosis and prevention of ovarian cancer. Methods: Bioinformatics analysis was utilized to identify immune-related feature genes in ovarian cancer. The GSE18520 and GSE40595 datasets were downloaded from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/) based on the gene expression comprehensive database, and the corresponding platform’s chip probe information was retrieved. GSE18520 served as the training set, and GSE40595 served as the validation set. A total of 2660 immune response genes (IRGs) were obtained from the ImmPort database (https://www.immport.org/home). Immune genes were screened and analyzed for feature genes using the “limma” package of R (4.2.1) software, and the results were visualized in a heat map. LASSO regression analysis and ssGSEA analysis were conducted to investigate the distribution of immune cell infiltration. Changes in regression coefficients of different genes in the model were also analyzed. Results: Five key genes—CLEC4M, DEFB1, LCN2, PTH2R, and LGALS2—were identified, and the correlation between these key genes and immune cells was analyzed. Conclusion: The findings indicate that CLEC4M, DEFB1, LCN2, PTH2R, and LGALS2 are significantly associated with various immune cell types, suggesting that these genes may regulate immune cell behavior and influence disease progression. This bioinformatics study provides a foundation for potential therapeutic targets in ovarian cancer; however, further clinical and experimental studies are required to validate the findings.