It is witnessed that building information modeling (BIM) technology has shown its capabilities in data integration in the construction industry. Incorporating innovative geotechnical theories into BIM helps to further develop its application potential. In the practice of deep excavation engineering, obtaining accurate soil parameters is the key to preventing deep excavation accidents and reducing construction costs. Aiming at the complexity of soil properties in soft soil deep excavations, an intelligent inversion framework for soil parameters in deep excavations is established by using BIM technology, finite difference method (FDM), and nondominated sorting genetic algorithm II (NSGA-II). Firstly, a building information modeling-numerical simulation (BIM-NS) integrated component is implemented based on a transformation interface, including geometric meshing processing and controlled script automated execution. Then, a back-analysis component based on NSGA-II optimization is attached to the BIM-NS processing to improve the accuracy of soil parameters. Subsequently, a framework of the building information modeling- numerical simulation-machine learning (BIM-NS-ML) integrated technology is established, enabling the usage of optimal soil parameters for automatic deep excavation simulation. Finally, the integrated framework is applied to a subway deep excavation project, which verifies that the proposed intelligent integration framework can accurately identify soil parameters in an efficient manner. The BIM-NS-ML integrated technology significantly improves the efficiency of modeling and calculating. The multi-objective optimization algorithm effectively addresses the problem of parameter complexity in soft soil. In addition, the intuitiveness of parameter inversion results is further enhanced to provide support for construction management and decision-making.