Abstract: The time-series monitoring and prediction of tailings dam stability have always been a major concern in China’s mine safety research. Focusing on a tailings dam in Anhui province，this study obtained 26 periods of longitudinal deformation data from six characteristic monitoring points on the dam surface，using InSAR and GNSS technologies. Based on the data，a least-squares adjustment model with restricted parameters was established. Combined with the initial three-dimensional coordinates of the monitoring points as polynomial correction parameters，the InSAR and GNSS data were fused to improve the data accuracy. Then，time-series prediction of deformation data was conducted for the monitoring points using the back propagation （BP） neural network，thus obtaining their future deformation data. Experiments were carried out to compute and compare the deformation data and corresponding root mean square error （RMSE） of each period before and after fusion，wherein the fused GNSS and InSAR data were evaluated with the root mean square error （RMSE） as the accuracy standard. The results showed that the post-fusion RMSE decreased by up to 70.61% and by at least 4.34% （average：25.91%），compared to pre-fusion data. Furthermore，the neural network model was used to repeatedly train the fused InSAR data from periods 1 to 22，with periods 23 to 26 serving as the test set，ultimately outputting the data of each point for periods 23 to 26. Compared to the GNSS data，the RMSE of the outputs were less than 1.5 mm. These results can provide reliable technical support for the time-series monitoring and prediction of tailings dam stability.