Abstract: Kalman filtering is used to filter GNSS mining subsidence monitoring data in order to solve the problems of long period and high frequency GNSS mining subsidence monitoring data that are greatly affected by the external environment, high data interference noise and low data reliability. Firstly, the regression analysis method is adopted to automatically divide the monitoring data into three parts: the initial stage, the active stage and the decline stage. The subsidence in the initial stage and the decline stage is relatively stable, and the subsidence data is filtered by the standard Kalman filtering model. The add correction kalman filter model was constructed to deal with the data in the active stage of rapid subsidence change. The filter program was established by Python language, and the monitoring data of five-year and hourly interval sampling rate in a mining area in Ningxia were calculated and analyzed. The results showed that the process curve of different kalman filtering results in different stages was consistent with the measured results, and the filtering effect was good. The add correction kalman filter model can effectively process the monitoring data of mining area with large subsidence variation. The method constructed in this paper can effectively reduce the impact of data fluctuation on the subsidence result and improve the reliability of monitoring data. The research results provide a scientific basis for long-term and high-frequency settlement monitoring data processing.