ABSTRACT The hysteresis model simplifies the relationship between resilience and deformation in seismic performance analysis of structural components. However, accurately capturing structural performance poses a challenge due to the intricate nature of hysteresis data. To enhance precision, this study introduces a multi-physics-guided neural network model. This innovative approach integrates explicit hysteresis models with an implicit network to create a robust hybrid model. The network architecture is grounded in physical principles, with the loss function incorporating physical parameter relationships for effective training. Through quasi-static testing, this approach demonstrates its robust adaptability and precision to the unique hysteretic curves of diverse components.