In recent years,the construction of large-scale engineering structures has involved numerous links in the construction process, including design, construction,supervision, material supply,and so forth.The coordination and cooperation between these links are of paramount importance. The presence of several linkages during the building process raises the possibility of communication breakdowns and information transfer delays, which can negatively affect the project's overall progress and quality. To overcome these obstacles, the study constructed a safety management model for the construction of large engineering structures by introducing digital twin technology. This model was then optimized and improved by using an inverse neural network algorithm based on particle swarm optimization. The outcomes of the improved model were subjected to testing, and the findings demonstrated that the model trained by the research exhibited an exceptionally high degree of accuracy in the test, with a prediction accuracy of 98.2%. Especially in the stress prediction of the cable, by comparing the predicted value of the research model with the actual value, it was found that the prediction accuracy was as high as 99.1%. In addition, the model also shows excellent performance in the eigenvalue system, and the EV value of the model is as high as 0.978. The model is also able to accurately identify the critical components under specific working conditions. From the above results, it can be observed that the research model has reached the expected standard in terms of performance and reliability,and has strong application value in the prediction of structural safety management of large-scale projects.