Angiogenesis related genes (ARGs) associated with the occurrence, development and prognosis of cervical cancer (CC) were screened using bioinformatics methods, and then the related prognostic risk model was constructed and verified. Firstly, the expression profile and clinical characteristics of CC patients were searched and differentially expressed ARGs were extracted from TCGA database. Through Lasso Cox regres-sion analysis, the ARGs for predicting prognosis were then selected to construct a relevant model. Further-more, external validation was performed with the GSE52903 and GSE44001 datasets. Finally, the mechanism of CC prognosis was discussed by gene set enrichment analysis (GSEA). Fifteen prognosis-related ARGs were selected out, including EFNA1, ITGA5, EPHB4, NRP1, CDH5, PLAU, BMP6, DLL4, JUN, CA9, MMP1, BA-IAP2L1, SERPINF1, F2RL1 and FGFR2. Kaplan-Meier survival curves based on GSE52903 and GSE44001 datasets showed that the overall survival (OS) (P=0.005) and disease-free survival (DFS) (P<0.001) of high-risk group were significantly lower than those of low-risk group. Receiver operating characteristic (ROC) curve analysis showed that, in the GSE52903 validation set, the area under the curve (AUC) values at 1 year, 3 years and 5 years were 0.84, 0.77 and 0.73, respectively, and the C-index was 0.72, and in the GSE44001 validation set, the AUC values were 0.71, 0.72 and 0.70, respectively, and the C-index was 0.70, indicating that the model has a strong predictive effect on the prognosis of CC patients. The pathways enriched by GSEA are mainly involved in DNA replication, extracellular matrix (ECM) receptor interactions, complement and coagulation cascades, the processes of which are closely related to cervical carcinogenesis and progression. These results suggest that the 15 key ARGs may be potential biomarkers for the prognosis of CC.