Abstract: The EEMD-LSTM model is proposed to enhance the prediction accuracy and stability of GNSS monitoring data for open-pit mine slopes, addressing challenges in traditional methods such as mode mixing, noise interference, and insufficient capture of long-term dependent features in handling complex nonlinear time series data. This model integrates ensemble empirical mode decomposition (EEMD) and long short-term memory (LSTM) networks. Firstly, the EEMD algorithm adaptively decomposes raw monitoring signals into multiple intrinsic mode functions (IMFs), effectively separating noise and sudden anomaly information to resolve the mode mixing issue inherent in traditional empirical mode decomposition (EMD). Secondly, the LSTM network extracts temporal features from decomposed IMFs and enhances long-term dependency modeling through its gate control mechanism. Lastly, an improved data isolation procedure (involving repeated decomposition and independent predictions) prevents information leakage, while multi-dimensional error evaluation metrics (MAE, MAPE, RMSE) validate model performance. Experimental validation utilized GNSS monitoring data from an open-pit mine in Heilongjiang Province, processing 6 727 displacement datasets to predict 30-day deformation trends. Results demonstrated that EEMD successfully isolated high-frequency noise (IMF1, IMF2) and low-frequency trend components (IMF8, IMF9), significantly reducing anomaly-induced prediction interference. The model exhibited optimal performance in 3D displacement prediction, achieving the highest precision in the z-direction (RMSE=0.017), though systematic bias in the y-direction requires further optimization. The improved isolation process notablely reduced errors in 2D/3D predictions, confirming the model’s resistance to information leakage. Correlation with slope stability calculations showed safety factor improvement to 1.21 after treatment, with monitoring point displacements stabilizing within 50 mm. By combining signal decomposition with deep learning, the EEMD-LSTM framework enables multi-scale analysis of complex time series features, providing a high-precision dynamic prediction tool for slope disaster early warning systems in open-pit mining environments.