Objective To construct a random forest algorithm model for predicting the risk of second-degree and above perineal laceration during delivery and to preliminarily evaluate the predictive performance of the model. Methods A total of 1,366 parturients who underwent vaginal delivery were selected as the study subjects using convenient sampling method. They were randomly divided into a training set and a validation set in a 7∶3 ratio. LASSO regression analysis was employed to screen the risk factors for second-degree and above perineal lacerations. A random forest algorithm was then used to build the prediction model, and various performance metrics such as the area under the ROC curve, predictive accuracy, sensitivity, and specificity were calculated to evaluate the model. Results A total of 8 predictive factors were included in the random forest model, namely pre-pregnancy BMI, weight gain during pregnancy, primiparity, history of Cesarean section, epidural anesthesia, induction of labor, artificial labor, and estimated fetal weight. Among them, estimated fetal weight had the greatest impact on second-degree and above perineal lacerations during delivery, followed by primiparity and induction of labor. The area under the ROC curve of the random forest model in the validation set was 0.698 (95% CI:0.645~0.751), with a predictive accuracy of 80.0% (95% CI:75.8%~83.8%), and sensitivity and specificity of 50.5% and 89.1%, respectively. Conclusion The risk prediction model for second-degree and above perineal laceration during delivery, based on the random forest algorithm, has certain predictive value. However, the predictive performance still needs improvement.