Objective To construct a risk predictive model of intraoperative hypothermia for patients undergoing laparoscopic surgery and to verify the predictive effect of the model. Methods 1043 patients who underwent laparoscopic surgery and met the inclusion and exclusion criteria were selected in our hospital from June to October 2020,using the convenience sampling method. They were randomly assigned to a modeling group and a verification group at a ratio of 7 ∶ 3. The influencing factors of patients with intraoperative hypothermia（n=407） and patients without intraoperative hypothermia（n=323） in the modeling group were compared,which is conducive to the random forest algorithm to sort the influencing factors and build the prediction model. Results The incidence of intraoperative hypothermia was 55.75% in the modeling group and 54.95% in the validation group. In the importance score of random forest algorithm variables,basic body temperature,operating room temperature,BMI,operation time and other indicators have a high contribution to the model classification,with clinical significance. The area under the receiver operating characteristic curve of the predictive model is 0.797;the sensitivity is 78.74%;the specificity is 64.03%;the accuracy is 72.20%. Conclusion The prediction model based on random forest algorithm is effective,which is of great significance to identify the key factors of intraoperative hypothermia in patients undergoing laparoscopic surgery and intervene timely and effectively.