Abstract: OBJECTIVE To establish the risk prediction model for postoperative surgical site infection in patients undergoing laparoscopy converted to open cholecystectomy so as to provide technical support for screening of high-risk population with surgical site infection（SSI）. METHODS A total of 216 patients who underwent laparoscopy converted to open cholecystectomy in department of minimally invasive surgery of Nanyang Second People's Hospital from Jan 2015 to Aug 2021 were enrolled in the study and divided into the SSI group with 54 cases and the non-SSI group with 162 cases according to the status of SSI. The clinical data were collected from the patients, the risk factors for SSI were screened out, and the risk prediction model for SSI in early stage was established. The receiver operating characteristic（ROC） curves were drawn for the risk prediction model, the areas under ROC curves（AUCs）, sensitivity and specificity were calculated, and the predictive effect of the risk prediction model was evaluated by verification of clinical cases. RESULTS There were significant differences in the C-reactive protein（CRP）, preoperative endoscopic retrograde cholangiopancreatography（ERCP） or percutaneous transhepatic bile duct drainage（PTBD） between the SSI group and the non-SSI group（P＜0.05）. The preoperative ERCP and preoperative PTBD were the influencing factors for postoperative SSI in the patients undergoing laparoscopy converted to open cholecystectomy（P＜0.05）. The study based on the model combined with multivariate logistic regression model showed that CRP, preoperative ERCP and preoperative PTBD may raise the accuracy in prediction of SSI（AUC=0.812）（P＜0.05）. The result of preliminary verification for 30 patients indicated that the sensitivity of the risk prediction model was 74.09%, with the specificity 85.21%, the positive predictive value 70.00%, the negative predictive value 90.00%. CONCLUSION The CRP, preoperative ERCP and preoperative PTBD are the risk factors for postoperative SSI. The model established based on the risk factors show remarkable effect on prediction of SSI.