Objective：This study aims to use the data from the Cancer Genome Atlas(TCGA)database to construct a survival prognostic model composed of m5C methylation⁃related genes，and to analyze the independent prognostic factors related to the survival of patients with renal clear cell carcinoma(RCCC)to predict the prognosis of RCCC. Methods：The data of patients with RCCC were downloaded from the TCGA database，m5C methylation⁃related genes were obtained from the published literature，and their differential expression between the tumor group and the control group was analyzed. Consensus clustering analysis was then performed to reveal the relationship between m5C methylation ⁃ related genes and prognosis of RCCC. A survival prognostic model was constructed by univariate Cox analysis and Lasso ⁃Cox regression analysis to analyze the prognostic factors associated with RCCC. Finally，GO and KEGG enrichment analysis was performed to further explore biological functions and potential signaling pathways. In addition，we also performed qRT ⁃PCR experiments to measure the differential expression of m5C methylation ⁃ related genes between cancer cells and normal cell lines or between RCCC and adjacent normal tissue. Results：We found that m5C methylation ⁃ related genes were differentially expressed between the tumor group and the normal group in RCCC. The results of consensus clustering analysis showed that the prognosis of patients with RCCC in cluster 1 was better than that in cluster 2.The constructed survival prognostic model divided RCCC into high⁃risk group and low⁃risk group. Univariate and multivariate Cox analysis showed that grade and stage may be independent prognostic factors for RCCC. GO and KEGG analysis showed that m5C ⁃ RNA modification helps to regulate the progression of RCCC. In addition，qRT ⁃ PCR experiments also confirmed that m5C methylation ⁃ related genes are differentially expressed between the tumor group and the normal group in RCCC. Conclusion：The survival prognostic model of m5C methylation⁃related genes constructed by this research can predict the prognosis of patients with RCCC.