Abstract: Abstract：Measures of subway tunnel safety monitoring and monitoring data analysis and prediction are important means to ensure the safety of subway tunnel. Due to the influence of construction environment， there are noise in the monitoring data inevitably. Taking the automatic deformation monitoring data of shield subway tunnel segments as the research object， a deformation monitoring data analysis and prediction method was presented based on ICEEMDAN-LSTM. Firstly， ICEEMDAN was used to decompose the monitoring data and obtain the IMF and residual components of the monitoring data. The LSTM network model was built， and it was used to predict the IMF and residual components of the monitoring data. Finally， the predicted values of IMF and residual components were superimposed and reconstructed to obtain the predicted values of deformation. The experimental results show that ICEEMDAN-LSTM model has higher prediction accuracy than BP and LSTM model.

Key words: Key words： LSTM, ICEEMDAN, deformation monitoring data, analysis and prediction, subway tunnel segments