With the vigorous development of social economy, the demand for large buildings such as subways, tunnels, and bridges is growing. Through analyzing the structural deformation data, it can judge the future development trend of the structure so that emergency measures can be taken in advance to prevent the occurrence of disasters. Due to the instability and nonlinearity of deformation monitoring data, the prediction of monitoring data has become a problem in structural monitoring researches. Aiming at the problems of structural deformation prediction models, a long and short-term memory network (LSTM) structural deformation prediction model is proposed based on orthogonal parameter optimization. The long-term memory of the time series can be obtained through the LSTM network structure, and the internal time characteristics of the structural deformation data can be fully mined, the parameters of the LSTM model can be optimized through the orthogonal experiment. Finally, the model was verified by measured data. Experimental results show that the predicted value of the model is closed to the actual monitoring value. Compared with the WNN, DBN-SVR, and GRU models, the average RMSE, MAE, and MAPE are reduced by 56.01%, 52.94%, and 52.78%, respectively. The LSTM structural deformation prediction model based on orthogonal parameter optimization proposed in this study is an effective structural settlement method, which provides reliable information for the safe construction and operation of the structure, and is of great significance to ensure the safety of the structure.