In view of the high incidence of intraoperative hypothermia and complex influencing factors in patients undergoing anesthesia， a prediction model of intraoperative hypothermia based on feature selection and XGBoost optimization is proposed to better assist doctors in the clinical diagnosis of patients. Firstly， the random forest （RF） is used to deal with the high-dimensional data sets， and features are selected by the RF out-of-bag estimation. Then， XGBoost hyperparameters are optimized using the genetic algorithm based on elite retention strategy， i.e.， EGA. Finally， the prediction is trained according to the optimal parameters and thus can be used to predict intraoperative hypothermia. This model combines the advantages of three algorithms to improve model generalization ability and prediction accuracy. The experimental result shows that the proposed model performs better other seven machine learning classification prediction models such as logistic regression， support vector machine， and so on in prediction accuracy， precision， recall and AUC， and overcomes the three representative hyperparameter tuning methods.