Abstract: Coastal cities of China are often affected by typhoons. Extreme rainfall brought by typhoons will cause serious flood and waterlogging if it encounters the high tide of storm surge. Before the arrival of typhoon, scientific and accurate analysis and prediction of storm tide level and timely and reasonable scheduling of projects are of great significance to reduce the risk of waterlogging disasters in coastal cities. Based on the analysis of the influencing factors of storm surges, the LSTM (Long-Short Term Memory) artificial neural network prediction models of Chiwan station and Nan'ao station are established. The results show that the influencing factors and pre time sequence n of storm tide level are different at different tide level stations. The error is the smallest by using the tide level and wind speed in the first 3 hours to predict the storm tide level at Nan'ao station, and predicted with the minimum error by using the tide level and wind speed in the first 6 hours at Chiwan station.