Purpose/Significance To investigate the application of cutting-edge technologies in predicting the incidence of hemorrhagic fever with renal syndrome (HFRS), to compile and integrate various time-series analysis methods, evaluate and select the optimal model. Method/Process By utilizing national HFRS incidence data from 2004 to 2020, the effectiveness of models is predicted based on statistical methods:SARIMA, STL-ARIMA and TBATS, neural network approaches:NNAR, LSTM and combined models of SARIMA-LSTM with 3 different weighting schemes. The performance of these models is comprehensively assessed using RMSE, MAE and MAPE. Result/Conclusion The SARIMA and LSTM models are identified as the superior individual models. The combined SARIMA-LSTM model demonstrates enhanced performance compared to individual models. The SARIMA-LSTM model optimized using the reciprocal of error method is deemed the optimal model. The optimal model is expected to provide technical support and references for the early warning system model design of HFRS.