Abstract: Abstract: Physics-Informed Neural Networks (PINNs), as a deep learning method incorporating physical knowledge, have shown great potential in addressing reactor core neutron physics problems within the field of nuclear engineering in recent years. However, PINNs still face limitations in terms of accuracy when solving these problems. To further improve the accuracy of PINN models, this paper proposes a Residual Network-based Physics-Informed Neural Network model, or ResNet-PINN. The fundamental principles and numerical computation processes of ResNet-PINN are elaborated in detail, and it is applied to solve neutron diffusion and transport equations. Experimental validation demonstrates that ResNet-PINN can significantly enhance the accuracy in solving reactor core neutron equations, effectively addressing the accuracy limitations of standard PINN models, and offers a degree of innovation in its approach.