Aiming at the dynamic characteristics of tailings dam displacement and deformation and the shortcomings of traditional prediction models in tailings dam displacement prediction, a tailings dam displacement prediction method based on time series decomposition reconstruction and SSA-LSTM-Attention model is proposed. First, the tailings dam displacement monitoring data is decomposed and reconstructed into trend terms and fluctuation terms through ICEEMDAN; secondly, on the one hand, the Gaussian fitting method is used to fit and predict the trend terms, and on the other hand, the fluctuation terms are calculated by the gray correlation degree. The relevant influencing factors were screened, and the attention mechanism was combined with LSTM to establish a fluctuation term displacement prediction model based on the attention mechanism and LSTM. At the same time, SSA was used to optimize the hyperparameters of the model. Finally, the trend term and the fluctuation term were combined. The superposition gives the total displacement prediction. Taking a tailings pond in the Panxi area as an example to verify the performance of the model, and compared with BP, LSTM, LSTM-Attention, and other models, the results show that the root means square error, mean absolute error and coefficient of determination obtained by this method are respectively 0.742mm, 0.553mm and 0.994, the proposed method can greatly improve the prediction accuracy of tailings dam displacement and deformation.