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 tech-nology.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 as-sociation 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,in-dicating 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.
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