Abstract: The shield cutterhead torque reflects the mechanical interaction characteristics between the cutterhead and the stratum. Accurately predicting torque changes in real-time can help adjust tunnelling parameters in advance, ensure smooth machine operation, and reduce cutting tool wear. Therefore, this paper proposes a deep learning model based on Temporal Convolutional Network (TCN) and Long Short-Term Memory (LSTM) for real-time prediction of cutterhead torque. The results indicate that the TCN-LSTM model can capture the local features of the input parameters and establish long-term dependencies, achieving the highest prediction accuracy compared to other models. The model performs stably in multi-step predictions, enabling longer lead-time predictions of cutterhead torque. A 4∶1∶1 data set split ratio yields the optimal performance for the prediction model.