Background and Aims Colorectal adenocarcinoma (COAD) is one of the major causes of cancer-related mortality, and accurate prediction of prognosis and assessment of survival risk factors in COAD patients are particularly important. MicroRNAs (miRNAs) extensively participate in regulating tumor biology by targeting downstream mRNA and have become promising biomarkers for application research. This study aims to identify prognostic miRNAs for COAD through bioinformatics methods and to construct a prognostic prediction model, providing references for COAD prognosis determination and individualized treatment planning.Methods Clinical information and miRNA-seq data of COAD patients were downloaded from the TCGA database to obtain differentially expressed miRNAs. Key prognostic miRNAs were obtained through univariate and multivariate Cox proportional hazard regression models, and a risk score calculation formula was constructed using the multivariate Cox regression model. The Kaplan-Meier method was used to analyze the survival status of high- and low-risk patients, and the sensitivity and specificity of the risk score were evaluated using ROC curves. Internal validation was performed by randomly selecting 50% of cases from the sample. The prognostic risk nomogram model was used to determine the clinical and pathological parameters and risk scores of COAD patients using a column diagram model. The Targetscan and miRDB databases were used to predict target genes of the prognostic miRNA model, and the String database was used to analyze protein-protein interactions.Results Differential expression analysis identified 320 miRNAs, among which 167 were upregulated and 153 were downregulated. Univariate and multivariate Cox proportional hazards regression analysis of the differentially expressed miRNAs revealed miR-503-5p, miR-335-3p, miR-185-5p, miR-4436b-5p, and miR-125b-2-3p as key prognostic miRNAs for COAD patients. The survival analysis results, combined with risk score, showed that patients with high-risk scores had significantly worse prognosis than those with low-risk scores (P=0.005 6), which was also validated in a randomly selected internal validation group (P=0.014). The area under the ROC curve of the 1-, 3-, and 5-year risk scoring models were 0.666, 0.724, and 0.707, respectively, while the values for the internal validation group were 0.681, 0.699, and 0.703, respectively. Cox regression analysis showed that the consistency coefficient for the predictive nomogram of COAD was 0.836. Univariate and multivariate Cox analysis showed that the risk score was an independent prognostic factor for COAD in the modeling group and the internal validation group (both P<0.01). The miRNA target gene prediction revealed 87 target genes, while the protein-protein interaction network analysis identified 10 key genes involved in protein interactions.Conclusion The COAD prognostic miRNA model and the nomogram constructed based on factors such as age, AJCC stage, T stage, radiotherapy and chemotherapy, and risk score can accurately predict the risk of COAD, providing a theoretical basis for identifying high or low-risk patients, accurately predicting prognosis, and assessing patient survival risk.