Abstract: Prediction of TBM tunnelling parameters with Bi-LSTM model is subject to shortcomings in model training time and convergence speed. So, an intelligent prediction model of attention-enhanced bidirectional long-/shortterm memory network (Bi-LSTM+EMB_ATT) formed by parallel fusion was proposed by improving the use of traditional attention mechanisms, and the complete tunneling cycle data was selected to predict the tunneling parameters in the stable section of TBM. The operation data of TBM3 Section of Jilin Songhua River Diversion Project were collected and divided into a training set and a test set and preprocessed by the binary state discriminant function and other methods, and then the Pearson method was adopted to analyze the results. The 21-dimensional tunnelling parameters of the complete tunneling cycle section was used as the input of the prediction model to compare and analyze the parallel-connected Bi-LSTM+EMB_ATT model and serial-connected Bi-LSTM+ATT model. The results show that the goodness of fit of Bi-LSTM+EMB_ATT model for predicting TBM tunneling parameters is above 0.91 with average absolute error less than 2.7%, which is higher than that of Bi-LSTM+ATT model.