Objective:To establish a blood consumption prediction model for emergency trauma patients based on machine learning algorithm, so as to guide blood collection and blood supply institutions to prepare for the early blood demand of mass casualties in public emergencies.Methods:A retrospective analysis was conducted on trauma patients in the emergency system database of 12 hospitals in Zhejiang Province from January 2018 to December 2020. Patients with chronic medical history such as hematological diseases and tumors, and transferred from other hospitals after external treatment were excluded. The patients were divided into the transfusion group and non-transfusion group according to whether they received blood transfusion. The differences in demographic and clinical characteristics between the two groups were compared, and the computer learning algorithm (XGBoost) was used to build the blood consumption prediction model and blood consumption volume prediction model of emergency trauma patients.Results:Totally 2025 patients were included in this study, including 1146 patients in the transfusion group and 879 patients in the non-transfusion group. The blood demand of emergency trauma patients mainly occurred within 3 days of admission (60%). The main variables affecting the blood consumption prediction model of emergency trauma patients were shock index, hematocrit, systolic blood pressure, abdominal injury, pelvic injury, ascites and hemoglobin. Compared with the traditional prediction model, XGBoost model had the highest hit rate of 59.0%. The accuracy of blood consumption prediction model was the highest when seven levels of blood volume were adopted, and the deviation fluctuated between [0~1] U. According to the prediction model, the blood consumption prediction formula was∑ nw× c. Conclusions:The preliminarily constructed prediction model of blood transfusion and blood consumption for emergency trauma patients has better performance than the traditional prediction model of blood transfusion, which provides reference for optimizing the decision-making ability of blood demand assessment of hospitals and blood supply institutions under public emergencies.