Objective：This study aims to compare the performance of seasonal autoregressive integrated moving average（SARIMA）model and long-short term memory（LSTM）model in predicting the epidemics of scarlet fever in Jiangsu Province，China，which may provide decision basis for prevention and control of scarlet fever. Methods：SARIMA model and LSTM model were fitted with data of monthly scarlet fever cases as well as meteorological variables during 2005-2017 in Jiangsu Province，China. Performance of the models were evaluated with data during 2018-2019. Results：The epidemics of scarlet fever in Jiangsu Province showed obvious seasonality，higherincidences were observed from April to June and from November to next January. SARIMA（3，1，2）（1，1，1）12 had the best perfor mance of all the alternative SARIMA models. The optimal LSTM model had 4 LSTM layers and 1 full connected layer，and each LSTM layer contains 32 memory cells. The mean absolute percentage error（MAPE）of SARIMA model and LSTM model in testingset were 22.47% and 16.94%，respectively，and the root mean squared error（RMSE）were 227.85 and 152.46，respectively. Conclusion：LSTM model performed well in predicting the incidence of scarlet fever in Jiangsu province. This model can be used to investigate the prevalence trends and assess the epidemic risk of scarlet fever，thus providing basis for optimizing and adjusting monitoring，prevention and control strategies and measures of this disease.