摘要Background Management of pediatric intestinal obstruction remains clinically challenging,particularly regarding the selection between surgical and conservative approaches.This study aimed to develop artificial intelligence(AI)models to support treatment decision-making.Methods A retrospective analysis was conducted on clinical data from pediatric intestinal obstruction patients.The dataset was split via stratified sampling(70％training/30％test),preserving outcome distribution.Predictive models incorporating clinical indicators were developed using machine learning,with evaluation metrics including accuracy,F1-score,Kappa value,positive predictive value(PPV),negative predictive value(NPV),precision-recall curves,calibration plots and decision curve analysis(DCA).Results Among 765 pediatric patients,425 responded to conservative treatment while 340 required surgery.The Random Forest model demonstrated optimal performance in the test cohort(area under the curve:0.953;sensitivity:0.879;specificity:0.901;accuracy:0.892;F1-score:0.878;Kappa value:0.780;PPV:0.878;NPV:0.905).Calibration,precision-recall,and DCAs indicated favorable clinical applicability.Conclusion Machine learning integration with clinical indicators shows potential as a decision-support tool for selecting surgical or conservative treatment in pediatric intestinal obstruction.