Taking a tunnel project under construction in Suzhou as the research background, this paper proposes a shield attitude prediction model and correction method based on the machine learning technology. Firstly, the spatial features of shield posture data were mined through a convolutional neural network. Then, the temporal features of data were mined through a bidirectional long short-term memory neural network. Afterwards, the important temporal feature information was mined through the attention mechanism. On the basis of the prediction results, the Apriori algorithm is introduced to extract the association rules of shield data, and the shield attitude correction method is proposed. Experiments show that the proposed prediction model in this paper has good generalizability. Compared to the three selec-ted baseline models, it achieves the smallest root mean square error and mean absolute error values, indicating higher prediction accuracy. Based on the attitude theory control model, a multi-loop attitude control model is constructed to obtain parameter suggestions for attitude adjustment, which provides a theoretical reference for intelligent attitude control.