<p>Deep learning methods have achieved significant progress in solving partial differential equations. However, when applied to the widely used anisotropic scattering neutron transport equations in reactor engineering, these encounter significant challenges. To address this issue, this study introduces a multi-antiderivative transformation alternating iterative deep learning method (M-AIM). This method transforms the integral terms of the scattering and fission sources in the transport equation into multiple antiderivative functions corresponding to the integrand, converts the differential–integral form of the transport equation into an exact differential equation, and establishes the necessary constraints for a unique solution. The M-AIM uses multiple deep neural networks to map the unknown angular flux density of transport equations and represents various forms of antiderivative functions. It constructs the corresponding weighted loss functions. By alternating iterative training with deep learning methods applied to these neural networks, the loss is reduced gradually. When the loss decreases to a preset minimum, the neural network approaches a numerical solution for both angular flux density and antiderivative functions. This paper presents a numerical verification of geometries such as flat plates and spheres. It verifies the validity of the theoretical framework and associated methods. The study contributes to the development of novel technical approaches for applying deep learning to solve anisotropic scattering neutron transport equations in reactor engineering.</p>