Postoperative pulmonary complications (PPCs) in children are common. However, few models tailored specifically for children are available to identify risk factors for PPCs and enable preoperative interventions. This study aimed to identify independent risk factors for PPCs in children and establish a risk prediction model.The clinical data of pediatric patients aged 0-6 years with an American Society of Anesthesiologists (ASA) physical status of I or II, and had undergone surgery with mechanical ventilation at Henan Provincial People's Hospital between January 2020 and December 2021 were retrospectively reviewed. Univariate and multivariate logistic regression analyses were employed to identify risk factors for PPCs. The corresponding nomogram prediction model was constructed based on the regression coefficients. The receiver operating characteristic curve and calibration curve were used respectively to evaluate the discriminant validity and calibration of the prediction model.Among 1545 patients included, 211 (13.4%) developed PPCs (156 of 1082 patients in the discovery cohort and 55 of 463 patients in the test cohort). In the multivariate logistic regression analysis, age (odds ratio [OR] 0.87, 95% confidence interval [CI] 0.79-0.96, P=0.007), mechanical ventilation time (OR 1.36, 95% CI 1.20-1.55, P<0.001), airway device (OR 1.67, 95% CI 1.04-2.68, P=0.033), ASA physical status (OR 1.96, 95% CI 1.34-2.88, P=0.001), and type of surgery (the total effect, P=0.004) were identified as the independent risk factors for PPCs in the discovery cohort. The prediction model showed good discrimination and calibration performance in both the discovery and test cohorts. The corresponding area under the curve was 0.762 (95% CI: 0.722, 0.803) and 0.818 (95% CI: 0.760, 0.875), respectively.We identified age, ventilation device and duration, ASA physical status, and surgical site as independent risk factors for PPCs in children aged 0-6 years. The predictive model performed well and demonstrated a certain capability in predicting the risk of PPCs.