Abstract: The safety management of the foundation pit is the key content of the construction of large-scale building foundation pits， and the displacement prediction of the foundation pit structure is an important means to prevent the maintenance accident of the foundation pit. However， due to the complex causes of local pit displacement in the pit， the existing support vector regression （SVR） and random forest （RF） methods ignore the characteristics of local weakening of the pit displacement with spatial displacement and accelerating growth with time local displacement， resulting in low prediction accuracy. Therefore， in this paper， a GA-BP neural network method that integrates the spatiotemporal attention mechanism （A-GA-BP） is proposed， which accurately represents the spatiotemporal dimensions and feature correlations of the foundation pit displacement prediction through spatiotemporal features， and improves the effectiveness of the foundation pit displacement prediction. Finally， taking a large-scale project in Suzhou as an example， this paper trains and evaluates the horizontal and vertical displacement monitoring data of the foundation pit， and quantifies the temporal features， spatial features and multi-order temporal and spatial features， and compares them with the existing methods. Experiment results show that the fitting index of this method is 29.19% and 41.25% higher than that of other methods， and the multi-order temporal and spatial features are 3.08% and 1.83% higher than the temporal or spatial features alone.

Key words: spatiotemporal attention mechanism, foundation pit displacement prediction, multi temporal and spatial features, BP neural network