With the continuous intensification of global climate change and the rapid development of urbanization, urban waterlogging disasters caused by extreme rainfall processes have become increasingly severe and have become a serious challenge faced by many cities around the world. Based on the rainfall data from 75 national automatic meteorological observation stations in Zhuji City, Zhejiang Province from May to August 2021 and the depth data of typical waterlogging points, a deep learning model Long Short Term Memory (LSTM) network is used to construct a relationship model between rainfall and waterlogging depth, providing a 2-hour urban waterlogging level forecast with an interval of 15 minutes in the future, The prediction results are compared with those of random forest (RF) and Artificial Neural Network (ANN) models. The prediction results show that LSTM has the best performance in predicting water accumulation in the next 2 hours using the water accumulation and rainfall data from the first 4 hours, with a root mean square error (RMSE) of less than 5.6cm, a correlation coefficient (CC) of over 0.93, and a Nash efficiency coefficient (NSE) of over 0.86. The prediction effect is superior to RF and ANN, indicating that the constructed artificial intelligence model for water accumulation prediction has good prediction performance.