Objective To construct a predictive model for the occurrence of lower extremity deep vein thrombosis(DVT)after internal fixation surgery for thoracolumbar fractures by using extreme gradient boosting(XGBoost).Methods Data of 220 patients who underwent internal fixation surgery for thoracolumbar fractures in the First Affiliated Hospital of Wenzhou Medical University from January 2019 to December 2022 was collected.The dataset was divided into a training set(154 cases)and a testing set(66 cases).The training set was processed by using the synthetic minority over-sampling technique and the predictive model was build based on XGBoost.The performance was compared on the testing set by using area under receiver operating characteristic curve,accuracy,F1 score,sensitivity and specificity.The interpretability analysis base on SHAP was conducted to quantify the degree of contribution of influencing factors.Results The XGBoost model outperformed logistic regression,support vector machine and random forest models on multiple metrics,with an area under the curve of 0.761 on the original testing set.The decision curve indicated that the XGBoost model has clinical application value.Conclusion The XGBoost model based on factors such as age,body mass index,and postoperative albumin,D-dimer,total protein,erythrocyte sedimentation rate,prothrombin time can effectively predict the occurrence of lower extremity DVT after internal fixation surgery for thoracolumbar fractures,which has good potential for clinical application.