With the rapid development of social economy, subways, tunnels, and bridges are occupying higher positions in people’s lives. Predicting and analyzing the structural deformation data of buildings and discovering hidden safety hazards in time have become indispensable for structural safety monitoring. Combining the advantages of Long Short Time Memory (LSTM), this study proposes a structural deformation prediction model based on Bidirectional Long Short Time Memory (Bi-LSTM). The model predicts the deformation data of the current node by memorizing the rules before and after the time node and fully mines the relevant information within the deformation data. Compared with WNN, LSTM, and GRU models, this model, with RMSE, MAPE, and MAE reduced by 66.0%, 61.2%, and 66.2% respectively, proves to be an effective method for predicting structural deformation.