Objective To explore the role of radiomics machine learning model based on biparametric magnetic resonance imaging (MRI) in the risk stratification of prostate cancer. Methods The clinical data of 128 patients with histologically proven prostate cancer were collected, including 60 cases in low-risk group (Gleason score ≤ 3+4) and 68 cases in high-risk group (Gleason score ≥ 4+3). All the patients were examined by 3.0 T MRI with the same parameters, and the clinical risk factors related to prostate cancer (age, volume of prostate lesions, location of lesions, prostate-specific antigen and prostate imaging reporting and data system[PI-RADS]score) were analyzed. The patients were randomly assigned (7:3) to training set or validation set for radiomics machine learning and verification. The radiomics features included gradient-based histogram features, morphological features, gray-level co-occurrence matrix (GLCM), gray-level run-length matrix (GLRLM), gray-level size zone matrix (GLSZM) and Haralick features. Multivariate logistic regression analysis was used to establish 3 prediction models to stratify the risks of prostate cancer:clinical model, radiomics model and clinical-radiomics combined model. The diagnostic performance and clinical benefits of each model were compared by receiver operating characteristic (ROC) curve and decision curve. Results The predictive efficacy of the radiomics model and the clinical-radiomics combined model in validation set were similar (area under curve[AUC]=0.78, 95% confidence interval[CI]0.63-0.93) and were better than that of the clinical model (AUC=0.75, 95% CI 0.60-0.91). Decision curve analysis showed that the radiomics model and the clinical-radiomics model had higher clinical net benefits than the clinical model. Conclusion Compared with only evaluating the clinical risk factors related to prostate cancer, the clinical-radiomics machine learning model based on biparametric MRI radiomics can improve the predictive accuracy of risk stratification of prostate cancer.