On June 18, the Tsinghua News Network reported that comprehending the constitutive behavior of materials under extreme conditions is essential for the modeling, design, and utilization of advanced metals and superalloys. Presently, the majority of constitutive models are based on phenomenological approaches. Although Crystal Plasticity Finite Element (CPFE) modeling can integrate physical equations at the microscale level, its extensive computational demands hinder its widespread adoption in engineering applications. Traditional mechanics research typically integrates theoretical analysis, numerical simulation, and experimental observation. Nevertheless, when confronted with high-dimensional parameter spaces, intricate multiscale coupling, and nonlinear experimental data, it often encounters obstacles such as exorbitant computational costs and stringent model assumptions. The incorporation of artificial intelligence (AI) and deep learning (DL) technologies is facilitating a transition in mechanics research from model-driven to data-driven methodologies.