Objective To construct a nomogram model to predict the risk of occurrence of puerperal infection after cesarean section based on single-center data. Methods Clinical data of pregnant women undergoing cesarean section in a hospital from January 2018 to January 2020 were analyzed retrospectively, univariate and logistic regression multivariate analysis were adopted to analyze independent risk factors for puerperal infection after cesarean section, relevant nomogram prediction model was constructed. Results Inflammatory infection of genital tract during pregnancy (OR=3.457, 95%CI: 1.205-9.917), gestational diabetes (OR=4.901, 95%CI: 1.247-19.259), premature rupture of membrane (OR=8.513, 95%CI: 3.041-23.830), postpartum recurrent vaginal bleeding (OR=10.000, 95%CI: 3.404-29.373), hemoglobin < 90 g/L (OR=4.657, 95%CI: 1.689-12.840) and albumin < 40 g/L (OR=5.163, 95%CI: 2.062-12.926) were all independent risk factors for puerperal infection in pregnant women after cesarean section (all P < 0.05). Based on the above 6 independent risk factors, a nomogram model for predicting puerperal infection after cesarean section for pregnant women was constructed, internal and external verification of the model showed that calibration curve of training set and verification set were well fitted to the ideal curve, predicted value was basically consistent with the measured value. C-index were 0.774 (95%CI: 0.739-0.809) and 0.765 (95%CI: 0.734-0.796) respectively, indicating that the nomogram model has good predictive ability. Conclusion There are multiple independent risk factors for occurrence of puerperal infection in pregnant women after cesarean section, nomogram model constructed in this study has good predictive ability and differentiation, which can be used for clinical screening of high-risk pregnant women and adopting effective nursing care.