To solve the problem of clinical inability to predict urinary stone types preoperatively, we propose a method for preoperative prediction of stone types based on CT images, and develop a preoperative prediction aid system for urinary stone types based on improved residual network. This enables preoperative prediction of stone types. The main tasks includes: firstly, Resnet34 is used as the base network with improved pooling layer, residual block structure and loss function, and a dense connection structure is added. Thus, the problem of stone misidentification is solved. Secondly, a twobranch multiheaded selfattentive module was designed to increase the feature weights of the stone region, which greatly improved the model performance. Finally, through a retrospective study, a dataset containing 5 709 CT images of stones was created and randomly divided into a training set, a validation set and a test set in the ratio of 3∶1∶1, which was used for training and performance validation of the model. The proposed improved residual network was experimentally verified to be 72.90% accurate on the test set, with a 6.38% improvement in accuracy and a 10% increase in F1 score. The results showed that the model can effectively improve the accuracy of preoperative prediction of urinary stones and has potential clinical application.