To achieve high-precision, full-coverage, all-weather satellite navigation, the performance of the system's services must be continuously and reliably monitored. A method of satellite navigation system service performance monitoring and analysis based on Long Short Term Memory(LSTM) neural network is proposed and implemented by using the monitoring data of monitoring stations around the world to solve and analyze position dilution of precision(PDOP) and positioning accuracy in order to further improve the accuracy and stability of satellite navigation system service monitoring and analysis. The experimental results demonstrate that, for PDOP, the mean accuracy of the results predicted by BDS and GPS based on LSTM is 5.15 and 3.89 percent higher than that predicted by ephemeris data, respectively; for positioning accuracy, the mean accuracy of the results predicted by BDS and GPS based on LSTM is 73.77% and 79.64% higher than that predicted by PDOP and user equivalent ranging error, respectively. It can be seen that the predictions made using the LSTM network outperform those made using ephemeris data in terms of prediction quality and localization accuracy. The method can effectively predict the future trend of data based on the historical data of PDOP and positioning accuracy, track the system service performance, and provide a reference basis for system service performance warning.