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Accurate prediction of tunneling speed in tunnel boring machine (TBM) construction is the basis for timely adjustment of operating parameters of TBM equipment to ensure safe and efficient tunneling. In this paper, a multi-step prediction model of TBM tunneling speed based on EWT-ICEEMDAN-SSA-LSTM hybrid model is proposed. Firstly, four data sets are selected under different geological conditions, and the original data are preprocessed using the binary discriminant function and the 3\(\sigma\)principle, and secondly, the preprocessed data are decomposed using the empirical wavelet variation (EWT) to obtain several subseries and residual series, and the residual series are decomposed again by the improved adaptive noise fully ensemble empirical modal decomposition (ICEEMDAN). Finally, several subsequences are substituted into the long and short term memory (LSTM) network with sparrow search algorithm (SSA) optimization for multi-step training and prediction, and the prediction results of each subsequence are summed to obtain the final results. The comparison with existing models shows that the performance of the proposed prediction method outperforms other models, and the average accuracy reaches 99.06%, 98.99%, 99.07% and 99.03% from the first step prediction to the fifth step prediction in four data sets, indicating that the method has high multi-step prediction performance and generalization ability, which can provide reference for other projects.