The key to improving the geological adaptability of TBM is to understand the interaction between the rockmass and the TBM operational data. Since the cutterhead loads, i.e., torque and thrust, are crucial for the safe operation of TBM, a deep learning model based on the two-stage attention mechanism has been established to explore the synergy effects of cutterhead loads with the changes of control parameters and rockmass during TBM excavation. The results show that (1) the proposed model predicts thrust and torque with R2 of 0.86, 0.75, and MRE of 0.25 AND 0.31, respectively. (2) Thrust and torque are highly dependent on the level of TBM cutterhead rotation speed and penetration rate. (3) The rockmass condition contributes about 10 percent to the accuracy improvement of the cutterhead loads prediction model.