In order to fully explore the time series correlation characteristics of storm surge and improve the forecast accuracy of storm surge, this paper comprehensively considered the influence of typhoon, meteorological and astronomical tide factors on storm surge, combined the advantages of long short term memory (LSTM) neural network and gray model (GM), and proposed a forecasting method of storm surge based on LSTM-GM neural network model. Using this method,12 historical typhoon data were used to simulate and forecast the storm surge in the Xiaoqing River Estuary, and the forecast results were compared with the prediction results of the LSTM neural network and the BP neural network. The results showed that compared to LSTM neural network and BP neural network, the NSE index of LSTM-GM neural network model increased by 6.5% and 11.4%, RMSE decreased by 70.6% and 72.2%, and MAPE decreased by 50% and 69.2%, respectively. The LSTM-GM neural network model can effectively handle the nonlinear relationship between storm surge and various influencing factors, and improve the accuracy of storm surge forecast.