Objective To explore the risk factors for postpartum pelvic floor dysfunction (PFD) based on pregnancy data and establish a Logistic prediction model. Methods A total of 221 parturients who underwent routine labor examination, full-term delivery and routine pelvic floor function measurement in our hospital from May 2022 to May 2023 were collected as the study objects, with the method of status investigation and retrospective cohort study. According to the ultrasonic measurement of postpartum pelvic floor, 129 cases were divided into PFD group and 92 cases were non-PFD group. Clinical data during pregnancy (including age, pre-pregnancy weight, delivery time, smoking history, drinking history, mode of delivery, duration of each labor stage, pregnancy complications, fetal birth weight, family history of PFD, chronic constipation and chronic cough, etc.) of the two groups were retrospectively analyzed. By using univariate analysis, Lasso regression analysis and Logistic multifactor regression analysis, a nomogram was constructed to establish a Logistic prediction model and screen out independent risk factors for postpartum pelvic floor dysfunction.Results Univariate analysis showed that there were statistically significant differences between the two groups in terms of age＞ 35 years, parity≥ 3 times, pre pregnancy BMI≥ 25 kg/m2, whether the delivery was cesarean section, second stage of labor ≥1 hour, PFD family history, newborn weight≥ 4000 g, combined with long-term cough and long-term constipation (P＜0.05). There was no significant difference between the two groups in smoking history, drinking history, pregnancy with hypertension, pregnancy with diabetes, the first stage of labor, and the third stage of labor (P＞0.05). Lasso regression analysis was conducted on 9 factors with differences in univariate analysis. Non zero feature predictive factors were included in Logistic multiple regression analysis. It was found that age>35 years old, parity≥ 3 times, pre pregnancy BMI≥ 25 kg/m2, second stage of labor≥ 1 hour, newborn weight≥ 4000 g, and family history of PFD were independent risk factors for postpartum PFD (P＜0.05). Cesarean section was a protective factor for PFD (P＜0.05). Based on the results of Logistic multiple factor regression analysis, a nomogram Logistic prediction model was established, and internal cross validation was performed on the prediction model. The results showed that the calibration curve, ROC curve, DCA curve, and clinical impact curve all indicated good accuracy of the model. Conclusion The nomogram Logistic prediction model established in this study can effectively predict the occurrence of postpartum PFD, and has good application value for clinical prevention of postpartum PFD and screening of high-risk populations for postpartum PFD