In deep learning methods, long and short-term memory networks (LSTM) and gated cycle units (GRU) are neural networks of two simulated time series, each with their advantages and disadvantages.In order to make up for their respective deficiencies and improve the prediction accuracy of river flow, the LSTM-GRU combination model is established and applied to the Baigou River basin of Daqing River system of Haihe River Basin.Based on the daily observation data of Dongci Village Hydrological Station from 2006-2019, the LSTM-GRU hydrological model was established with the observation data of 8 hydrometeorological factors (air pressure, water temperature, relative humidity, precipitation, sunshine, ground temperature, wind speed and water level) as the input and the river flow as the output.Among them, 2006-2015 data were the training set, 2016-2019 data were the validation set, and the root mean square error and deterministic coefficient were used as the evaluation indicators.To verify the model advantages, the simulation results of LSTM-GRU were compared with those of LSTM and GRU, respectively. The results show that the stability and accuracy of the LSTM-GRU combination model are significantly better than the single LSTM with GRU model, providing a new way for river flow prediction.