Objective: To construct a risk prediction model for poly victimization (PV) among rural left behind middle and high school students in Chaoshan, and to evaluate the prediction effect of the model, so as to provide scientific basis for early identification and prevention of PV among students. Methods: A questionnaire survey was conducted among 1 005 left behind students, selected from 7 middle and high schools in rural areas of Shantou City and Jieyang City by a stratified random cluster sampling method from January 2020 to September 2021, for the personal, family, external environmental factors, psychological factors (mental resilience, coping approaches, self esteem and social support) and PV situations. R software and Logistic regression were used to screen predictor variables to build a risk prediction model, and the area under the ROC curve (area under the curve, AUC), accuracy, precision, recall, F1 value and calibration curve were used to evaluate the model s effect. Results: The incidence rate of PV among left behind middle and high school students was 23.38%. The results of Logistic regression analysis showed that physical illness or disability ( β =1.02), grade retention during the past year ( β =1.31), having no close partner ( β =1.00), self harm intention (seldom: β = 0.58 , occasionally: β =0.79), negative peer behavior ( β =0.90), family member smoking ( β =0.59), criminal offenses of parents ( β =1.04), witnessing school bullying ( β =0.78), house moving ( β =0.58), using venting ( β =0.34) and the coping style of patience ( β =0.28) were positively correlated with PV among left behind children in Chaoshan area, and family support in psychological flexibility ( β =-0.31) was negatively correlated with PV ( P <0.05). A nomogram prediction model was constructed for the meaningful variables included in the multivariate analysis, and the prediction model AUC was 0.88, the accuracy was 82.00 %, the precision was 77.78%, and the F1 value was 43.75%. The calibration plot fitted well, and the model had good discrimination and calibration. Conclusion The risk prediction model for left behind middle and high school students with PV has good predictive performance and is helpful for schools and communities to early identify high risk middle and high school students with PV.