Abstract: The paper explores the combination method based on machine learning model and infectious disease model to predict influenza trend， and provides advice for medical institutions to take preventive measures. To precisely capture the temporal features of influenza-like illness （ILI）， this paper proposes a combined prediction model （LSTM-SIR-EAKF） based on long and short-term memory（LSTM）neural networks， Suceptible-Infected-Recovered（SIR）model， and Ensemble Adjustment Kalman Filter（EAKF）. Firstly， the model of LSTM is employed to learn the temporal relationship between ILI. Then， SIR model is used to simulate the transmission process of ILI. Finally， EAKF correctes the anticipated values of ILI from SIR model to obtain the final prediction values of ILI. The experimental results show that through the prediction of ILI in three time periods， the goodness of fit（R2）proposed by the LSTM-SIR-EAKF model are 0.996， 0.991 and 0.995， respectively， and the evaluation indicators of the prediction results are better than the comparison model. LSTM-SIR-EAKF model makes long-term prediction of influenza in time through long and short term memory network， and the infectious disease model simulates the changes of influenza population in space， effectively improving the prediction effect.

Key words: ILI prediction, LSTM, SIR, ensemble adjustment Kalman filter, time series