Objective ·Patients with acute myocardial infarction (AMI) undergoing percutaneous coronary intervention (PCI) are at risk of severe complications, such as hypotension and malignant arrhythmias, which directly affect procedural success and patient prognosis. Current clinical practice lacks targeted assessment tools, and traditional assessment tools have limitations in predictive efficacy. This study innovatively applies machine learning methods to construct a precise intraoperative complication prediction model, addressing the insufficient non-linear relationship capture in existing scoring systems and providing an early risk warning tool for surgeons. Methods ·The study included 811 emergency PCI patients who were treated at Shanghai Fengxian Central Hospital from 2019 to 2022, and defined 53 candidate variables. A multi-stage feature engineering framework was employed, including univariate screening, Spearman rank correlation analysis verification, and SHapley Additive exPlanations (SHAP)-based stability optimization. The prediction model was ultimately constructed using the eXtreme Gradient Boosting (XGBoost) algorithm.The primary endpoint was a composite of intraoperative complications, including hypotension, malignant arrhythmias, and severe bradyarrhythmias. Results ·The model identified six core predictive factors: lowest left ventricular ejection fraction (LVEF), culprit vessel in the right coronary artery (CVRCA), culprit vessel presence (CVP), culprit vessel in the left anterior descending artery (CVADA), B-type natriuretic peptide (BNP), and heart rate (HR). The XGBoost model achieved areas under the curve (AUCs) of 0.88 and 0.84 in the training and validation sets, respectively. Conclusion ·This study successfully constructed, for the first time, an intraoperative complication prediction model for AMI patients undergoing PCI based on the XGBoost algorithm. Its predictive performance significantly outperforms that of traditional scoring systems. By automatically selecting key clinical features and effectively capturing complex interactions between variables, the model provides individualized risk assessment for surgeons, thereby supporting clinical decision-making and potentially improving procedural success rates and patient prognosis.