Objective To explore the risk factors for postoperative pneumonia in patients with acute type A aortic dissection (AAAD) and construct a predictive model for postoperative pneumonia in AAAD patients. Methods Patients who underwent emergency surgery for AAAD at Xiangya Hospital of Central South University from January 2014 to June 2020 were retrospectively selected for the study. Predictive factors were screened by all-subsets regression, and the cut-off values for continuous data were determined by the Youden index. A binary logistic regression model was constructed to predict postoperative pneumonia in AAAD patients. The efficacy of the model was assessed by the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve. Results A total of 210 AAAD patients were included, 53 (25.24%) of whom developed postoperative pneumonia. Six predictive factors were screened through all-subsets regression, and the cut-off values were determined by the Youden index: male patients (OR=2.21, 95%CI: 0.88-5.54), chronic pulmonary disease (OR=2.53, 95%CI: 1.12-5.74), platelet distribution width >17.5% (OR=3.27, 95%CI: 1.57-6.78), surgical duration >9 hours (OR=2.76, 95%CI: 1.25-6.06), mechanical ventilation duration >99 hours (OR=3.87, 95%CI: 1.63-9.18), and red blood cell transfusion >9 units (OR=1.69, 95%CI: 0.80-3.60). The AUC of the constructed predictive model for postoperative pneumonia was 0.789 (95%CI: 0.718-0.860), and the optimal risk threshold was 0.21. The calibration curve and Hosmer-Lemeshow test (P=0.48) demonstrated good calibration of the mo-del. The decision curve showed that patients with a predicted postoperative pneumonia risk of 0-69% could benefit from using this model. Conclusion The predictive model constructed in this study can effectively predict the risk of postoperative pneumonia in AAAD patients. Early intervention can be implemented for high-risk patients in clinical practice.