Objective In this research, a potential prognostic marker for intrahepatic cholangiocellular carcinoma (ICC) was explored through a bioinformatics approach, and a survival prediction model was constructed to better guide clinical management. Methods The TCGA-CHOL and GSE107943 datasets were used to search for differential genes, and a scale-free network was constructed based on the Weighted Correlation Network Analysis (WGCNA) algorithm to search for gene modules that were strongly associated with tumourigenesis. The differential genes were intersected with genes within the modules to construct a prognostic risk model for ICC by one-way COX and Lasso-cox regression models, and the E-MTAB-6389 dataset was used for external validation of protein expression in 13 ICC paired tumour samples for the key genes involved in the model construction. Additional clinicopathological data were collected from 80 post-surgical ICC patients at our institution from June 2017 to June 2021, and patient survival data were obtained by telephone follow-up. Protein expression of independent prognostic risk genes was scored according to an immunohistochemical semi-quantitative approach, and patients were classified into high and low expression groups using a dichotomous approach to compare the overall survival of patients in different subgroups, Overall Survival (OS) and the relationship between independent prognostic risk genes and clinicopathological characteristics were analyzed. Results The differential genes were intersected with the genes in the blue gene module in WGCNA to obtain 958 genes. Five key genes were obtained by univariate Cox and Lasso- Cox regression analysis for model construction (CFH, EGR4, RERG, PRICKLE1, NIPA1). OS was significantly lower in high-risk patients than in low-risk patients. The model efficacy was evaluated and the area under the time-dependent ROC curves were: 0.858, 0.881, and 0.975. The calibration curves were evaluated against the column line plots, suggesting a high accuracy of the column line plots. It was also validated by an external cohort E-MTAB-6389, again indicating good model accuracy. CFH can be an independent prognostic risk gene for ICC, and CFH gene protein expression is associated with distant metastases, lymph node metastases, and TNM staging. Conclusion In this research, a 5-gene prognostic model is constructed, which has good predictive efficacy and can be used as a reference for prognostic assessment of ICC patients