[Objective] In traditional electromagnetic field simulation software, numerical calculation methods such as finite difference and finite element method are mainly used to solve problems. Although these methods can obtain numerical solutions closer to the experimental results, their computational accuracy heavily depends on the number of meshes and the quality of the dividing. While improving the solution accuracy, it also leads to a significant in computation time and cost, especially when using software for large-scale optimization design. [Methods] Therefore, this paper proposed a development strategy for electromagnetic simulation software that integrated artificial intelligence technology. Artificial neural network (ANN) models were used in pre-processing, solving, and post-processing to accelerate the entire solving process. In the modeling process, multimodal parametric modeling techniques based on images, speech, and text were used. In the mesh dividing and matrix solving, ANN models were used for classification judgment or regression prediction. In the processing and visualization stages of calculation results, machine learning fitting and interpolation methods were used for smoothing the computational results and improving the resolution. [Results] Based on electromagnetic simulation software, a large amount of finite element data could be obtained for specific problems. In a data-driven environment, it was possible to achieve the prediction of electromagnetic field distribution, the prediction of AC copper consumption based on surrogate models, the full performance prediction of motors with multiple input/output and operating conditions, multi-objective accelerated optimization with the help of classifiers, as well as multi-objective optimization and motor modeling based entirely on surrogate models. [Conclusion] This study constructs digital twins of electromagnetic products through data-driven approaches, providing effective support for their status monitoring, predictive maintenance and performance optimization.