Objective: To explore the correlation between hypoxia-related long non-coding RNA (lncRNA) and the prognosis of hepatocellular carcinoma (HCC) patients, and to construct and validate a prognostic risk model based on hypoxia-related lncRNA. Method: Obtain RNA transcriptome sequencing data and clinical follow-up data of HCC patients from the Cancer Genome Atlas (TCGA) database. Based on the HALLMARK-HYPOXIA gene set in the Molecular Signature Database (MSigDB), Spearman correlation analysis was used to screen hypoxia-related lncRNAs. Further, single-factor Cox proportional hazards regression, Least Absolute Shrinkage and Selection Operator (LASSO) - Cox regression, and multiple-factor Cox regression were used to screen for key lncRNAs associated with HCC prognosis, and a risk scoring model was constructed based on these results. According to the median risk score, patients were divided into high-risk and low-risk groups. Kaplan Meier survival analysis was used to compare the overall survival differences between the two groups, and the discriminant ability and clinical application value of the model were evaluated through a time-dependent receiver operating characteristic (ROC) curve and consistency index (C-index). Further combining clinical pathological characteristics for univariate and multivariate Cox regression analysis, screening for independent prognostic factors, and constructing a column chart prediction model. Result: A total of 367 HCC patients were included. Finally, 14 hypoxia related lncRNAs closely related to the prognosis of HCC were screened, namely MAPKAPK5-AS1, KDM4A-AS1, LINC00674, HCG15, NEAT1, EIF3J-AS1, BSG-AS1, FRMD6-AS1, HMMR-AS1, MRVI1-AS1, SZT2-AS1, SLC1A5-AS, CPS1-IT1 And AC115619, and based on this, construct a prognostic risk scoring model. The Kaplan Meier survival analysis results showed that the overall survival rate of high-risk group patients was lower than that of the low-risk group, and the difference was statistically significant (P < 0.01). ROC curve analysis showed that the area under the curve (AUC) of the model for predicting the survival of HCC patients at 1, 3, and 5 years was 0.59, 0.61, and 0.65, respectively, indicating that the model has good survival prediction performance. Multivariate Cox regression analysis showed that age, T stage, and risk score were independent risk factors affecting the prognosis of HCC patients (P<0.05). The Nomogram model was constructed on this basis. The calibration curve results confirm that the model has good predictive value for the 1, 3, and 5-year survival probabilities of HCC patients. Conclusion: 14 hypoxia-related lncRNAs are closely related to the prognosis of HCC patients. The risk scoring model constructed based on them has good prognostic stratification and survival prediction ability, and can provide a reference for individualized risk assessment and clinical decision-making of HCC patients.