Objective To explore the risk factors for healthcare-associated infection (HAI) in lung transplant recipients (LTRs), and construct a predictive nomogram model. Methods Clinical data of patients who underwent lung transplant in Wuxi People’s Hospital from January 2019 to December 2023 were analyzed retrospectively. The patients were divided into a training set (n=506) and a validation set (n=218). Independent risk factors were screened through LASSO regression, and multivariate logistic regression was included to construct a nomogram prediction model. The discrimination, calibration, and clinical applicability of the model were evaluated using receiver operating characteristic (ROC) curves, Hosmer-Lemeshow goodness-of-fit, and decision curves. Results Among the 506 LTRs, 201 developed HAIs, with an incidence of 39.72%. The major infection site was lower respiratory tract, and the major pathogen were Gram-negative bacilli (Acinetobacter baumannii). Older age, use of extracorporeal membrane oxygenation (ECMO), double-lung transplant, surgery duration >3 hours, long duration of continuous fever, frequent abnormal blood routine examination, and long duration of combined use of antimicrobial agents were identified as independent risk factors for HAI after lung transplant. The ROC curve analysis results showed that the areas under the curve (AUCs) of the training set and the validation set were 0.74 (95%CI: 0.70-0.78) and 0.71 (95%CI: 0.64-0.78), respectively. The Hosmer-Lemeshow test results showed that there was no statistically significant difference between the predictive and actual probability of HAI (P>0.05). The clinical decision curve results indicated that the modelhad clinical benefits at a threshold probability value of 7%-71%. Conclusion The nomogram prediction model constructed in this study can effectively evaluate the risk of postoperative infection in LTRs. The model is stable and has high clinical application value, providing scientific reference for postoperative infection prevention and control.