Abstract: Tropospheric delay is a vital factor that influence the measurement accuracy of GNSS. To solve the problems of poor stability and low accuracy of existing tropospheric delay model, a prediction model of tropospheric delay based on the Long-Short Term Memory neural network (LSTM) of Keras platform is proposed in the absence of measured meteorological parameters. Eight stations evenly distributed around the world were selected to use their 42-day hourly tropospheric delay data from the 90th to 131st day of 2016 to predict their hourly data of 132nd to 136th day. Based on the troposphere products provided by International GNSS Service (IGS) center, the prediction effects of LSTM model and back propagation neural network (BP) model were analyzed and compared. The result shows that the root mean square error of LSTM model basically reaches mm level, and the mean absolute error and mean absolute percentage error of LSTM model are lower than those of BP model, and the accuracy and stability of LSTM model are significantly improved compared with BP model. LSTM model has an average RMSE of 7.82 mm in mid and high latitude, which shows it is more suitable for mid and high latitude.