Objective To investigate the influencing factors of antibiotic-associated diarrhea （AAD） following congenital heart disease （CHD） surgery in pediatric patients， develop a nomogram-based predictive model， and validate its efficacy. Methods A retrospective analysis was conducted on the clinical data of pediatric patients who underwent CHD surgery in the Pediatric Intensive Care Unit （PICU） of a tertiary hospital in Guangdong Province from July 2022 to July 2024. Patients were categorized into an AAD group and a non-AAD group. Univariate and multivariate logistic regression analyses were performed to identify risk factors for AAD occurrence following CHD surgery. A risk prediction model was developed， and a nomogram was constructed. The predictive performance of the model was evaluated using the Receiver Operating Characteristic （ROC） curve to calculate the area under the curve （AUC）， the Hosmer-Lemeshow goodness-of-fit test， calibration curves， and clinical decision curve analysis. External validation of the model was conducted using data from patients in the Surgical Intensive Care Unit （SICU）. Results The incidence of AAD following CHD surgery was 48.52% （229 out of 472 cases）. Risk factors for AAD included the combined use of antibiotics， mechanical ventilation， elevated C-reactive protein levels， prolonged surgical duration， and extended antibiotic usage time （all with OR > 1， P < 0.05）. Conversely， probiotic administration was identified as a protective factor （OR < 1， P < 0.05）. The predictive model demonstrated excellent discrimination， as evidenced by the ROC curve areas： 0.922 （95% CI： 0.894 ~ 0.951） in the modeling group， 0.886 （95% CI： 0.838 ~ 0.915） in the internal validation group， and 0.862 （95% CI： 0.784 ~ 0.941） in the external validation group. Additionally， the model exhibited satisfactory calibration， as indicated by the Hosmer-Lemeshow test results： χ² = 7.96， P = 0.538 in the modeling group； χ² = 4.24， P = 0.895 in the internal validation group； and χ² = 9.923， P = 0.270 in the external validation group. Furthermore， the model provided significant clinical utility. Conclusions Combined antibiotic use， duration of antibiotic therapy， mechanical ventilation， surgical duration， C-reactive protein （CRP） levels， and probiotic administration are key factors influencing the occurrence of AAD. The risk prediction model developed based on these variables demonstrates robust predictive performance and can serve as a valuable reference for the development and implementation of preventive and therapeutic strategies in clinical practice.