In order to meet the demand for segment floating prediction in shield construction and the problem of insufficient training data for deep learning models, a set of shield segment floating prediction model was proposed by combining the tunneling mechanism of the shield machine with the process of segment floating. The numerical simulation software was used to simulate the process of segment floating of the shield structure, and using the large amount of numerical simulation data and the engineering field data for the deep learning training, so as to realize the data enhancement of the segment floating prediction model. The prediction model consists of the tube sheet floating process. The prediction model consists of a segment floating prediction model and two auxiliary models, which consider the interaction of active control and passive response parameters. Finally, a typical case study was carried out based on the shield section of the Beijing East 6th Ring Road Rehabilitation Project, and the results show that the prediction accuracy of the model is controlled within 4 mm, which meets the project requirements. The grouting parameters of the shield tail have the greatest influence on the model performance, followed by the digging parameters, and the shield attitude parameters have the smallest influence. Moreover, the training data of the segment floating based on the numerical simulation data can improve the prediction accuracy of the prediction model by 30%, which proves the effectiveness of the data enhancement method. The effectiveness of the data enhancement method is demonstrated. The data enhancement method based on numerical simulation data proposed in the article provides a new idea for the training and optimization of similar deep learning models.