The development of the transportation industry can effectively accelerate the speed of economic development, in which bridges occupy an important position in transportation. The safety of the bridge design and construction process is a key part of bridge construction, and relying on human resources to investigate safety hazards greatly affects efficiency. In this paper, we combine deep learning technology and the BIM model to explore the synergistic effect of both on the quality management of the bridge construction phase and analyze the measured data. The results show that the application of the BIM model can improve efficiency by 35% compared with the traditional 2D CAD drawings, and the accuracy of data analysis can be improved by 12.51% and 14.26% for DNN and DBN models based on deep learning, respectively. The addition of the GSO algorithm leads to a further 19.19% improvement in the training accuracy of the coupled model. Finally, the optimization model was used to analyze the load factors and force majeure factors that affect the safety of the bridge, and to find the structural factors that affect the safety of the bridge design, which guides to ensure the quality of the bridge during the construction process.