Proteins serve as the fundamental building blocks of life processes. Their functions are highly determined by the spatial structure, making analyzing these spatial structures essential for fully comprehending their roles. The application of artificial intelligence (AI) models has significantly advanced the development of algorithms for predicting protein spatial structure. AlphaFold2 represents a groundbreaking advancement in this field, enabling rapid, accurate, and large-scale predictions of protein spatial structures. In the AlphaFold era, substantial progress has been made in fields such as protein language model development, protein-protein interaction prediction, and protein design, with notable models including ESM2, ScanNet, RFdiffusion, and RoseTTAFold-All Atom, among others. These novel AI-based algorithms have profoundly facilitated research into protein function, disease mechanisms, and drug design.