Abstract: Coal main adit is susceptibility to external factors, so it is crucial to monitor and predict its deformation. Based on the application of fiber optic monitoring for adit deformation, A multi-step prediction model is proposed which is built with Ensemble Empirical Mode Decomposition (EEMD) integrated with TCN-LSTM deep learning network for adit deformation. Firstly, the monitoring data containing noise is decomposed into several Intrinsic Mode Functions (IMF) components using the EEMD method. Then, the fuzzy entropy of each IMF component series is calculated, and effective IMF components are selected. Finally, the TCN network is used to extract long-term features from different effective component series, while the LSTM network captures nonlinear features. The prediction results of each component are combined. The multi-output strategy is adopted in the training process of the prediction model, and the output is the fiber optic monitoring value for multiple times in the future. Experimental results on different fiber optic grating sensors show that the EEMD combined with fuzzy entropy method can filter out more noise while retaining the roadway deformation information. Compared with existing methods, the proposed prediction method has a coefficient of determination (R²) of 0.99, and the root mean square error (RMSE) and mean absolute error (MAE) are reduced by 3.0%−10.0% and 5.0%−20.0% in single-step prediction, respectively, resulting in more accurate predictions. Under the multi-output strategy, the average R² of this proposed method for three steps ahead is 0.95, and the RMSE and MAE values of the strain gauge are reduced by at least 75.0% and 31.5%. The RMSE and MAE values of the displacement meter were reduced by at least 50.0% and 66.7%, respectively, while the RMSE and MAE values of the pressure gauge were reduced by at least 85.7% and 62.3%. the proposed prediction method with multi-output strategy has the lowest error accumulation. The EEMD-TCN-LSTM multi-step prediction method for adit deformation provides a technical basis for predicting the deformation of roadway surrounding rock.