Objective To establish a prognosis model using immune-related genes in hepatocellular carcinoma (HCC) patients, which could provide a theoretical foundation for HCC immunotherapy. Methods Immune-related genes were identified by differential expression analysis, and risk prognosis prediction models were established using univariate, multivariate Cox and Least absolute shrinkage and selection operator (LASSO) regression analysis. The predictive value of the prognostic model was evaluated using the concordance index (C-index), receiver operating characteristic curve (ROC curve), and calibration curve and decision curve. In addition, risk score was used to stratify patients to assess prognostic differences in patients at different risk levels. Results We identified 1403 immune-related genes, mainly involved in biological processes such as immune response, adaptive immune response, and immunoglobulin production, as well as pathways such as cytokine interactions, chemokine signaling pathways and allograft rejection. Univariate Cox analysis found that 53 immune-related genes were associated with prognosis, and eight prognostic immune-related genes cytochrome P450 1A2(CYP1A2), ficolin 3(FCN3), hepatoma derived growth factor-like 1(HDGFL1), lipocalin 2(LCN2), mitochondrially encoded cytochrome C oxidase II pseudogene 12(MTCO2P12), peptidyl arginine deiminase 3(PADI3) and regulator of G protein signaling 16(RGS16) were further screened by LASSO and multivariate Cox regression analysis. Subsequently, a prognostic nomogram based on risk score was established. The ROC curve, calibration curve and decision curve confirmed that the model has good discrimination, accuracy and clinical value. Furthermore, stratified analysis showed that patients with higher risk scores had poorer prognosis. Conclusion We establish a prognostic nomogram model using eight immune-related genes, which can reliably predict the prognosis of HCC patients.