With the passage of time, the constant changes in relevant factors, and the daily maintenance of tailings ponds, the difficulty of tailings pond safety management is increasing day by day. In order to systematically improve the early warning ability for tailings pond dam break risk, the relationship between and influence of various related dam break risk factors of tailings ponds are utilised and the combination with dual attention is innovatively proposed. The risk prediction model for tailings ponds, EEMD-DA-LSTM, is improved. First, Pearson correlation coefficients are used to analyse the correlation between risk factors of tailings ponds. Then, the EEMD method is used to decompose the nonlinear displacement sequence, and the weights of input features are dynamically adjusted by double attention (DA). Finally, the LSTM network model is constructed to predict the displacement change. Taking valley-type tailings pond WKB-1 and mountainside tailings pond WKB-2 as examples, the dam break risk prediction models for tailings ponds are constructed based on three different models, the prediction results of different models are compared and analysed, and the prediction accuracy of the models is evaluated by three different evaluation criteria. The research results show that the integration of the EEMD-LSTM model with the DA model, that is, the EEMD-DA-LSTM model, has a better prediction effect for the dam break risk of tailings ponds WKB-1 and WKB-2 than other models through experimental verification. Therefore, the EEMD-DA-LSTM model is of great significance for preventing and resolving the safety risks of tailings ponds. It is valuable for practitioners in the mining industry and environmentally sustainable development.