〔Abstract〕 To screen the risk factors of cesarean scar diverticula (CSD) after cesarean section and to construct a risk prediction model . Methods 491 cases of mothers who underwent cesarean section were recruited as the study subjects , and the data from the database of negative ultrasound of mothers who returned to the hospital 12 months after operation were collected , and the dataset was randomly divided into the training set and the test group according to 7 ∶3 ; the variables were screened to obtain the risk factors of CSD and the risk prediction model was constructed by the use of least absolute shrinkage and selection operator (LASSO) ; the variables were screened using the LASSO to obtain the characteristic variables , and the characteristic variables were analyzed by multifacto- rial logistic regression analysis , and the nomogram prediction model was constructed by using the R software . Re- sults A total of 491 cases of sample data were included , including 344 cases in the training set and 147 cases in the test set; feature variables were screened by LASSO , and ten - fold cross - validation was used . Five variables were finally screened : number of cesarean deliveries , number of years between two cesarean deliveries , 24-hour hemorrhage , operation time and uterine position(P < 0. 05) . The accuracy of the decision analysis curves for inter- nal evaluation and internal validation of the CSD risk prediction model constructed using it was high; the AUC (95% CI) of the diagnostic model in the training set and the test set were 0. 75 (0. 71 - 0. 80) and 0. 79 (0. 71 - 0. 87) , respectively. Conclusion The risk prediction model established using the LASSO cross-validation algo- rithm has good predictive value for the occurrence of postpartum scar diverticula , which deserves clinical attention .