Abstract: This paper proposes a new method which combines physics-informed neural networks (PINN) with traditional source iteration method to solve few group neutron diffusion equations. And this paper uses Anderson acceleration method to accelerate the iterative process. The results of numerical examples such as two-dimensional multi material and three-dimensional single material show that the combination of PINN and traditional source iteration method can calculate the continuous neutron flux density distribution while ensuring calculation accuracy. The use of Anderson acceleration method can reduce the number of iterations and successfully achieve the forward solution of the few group neutron diffusion equations, which promotes the application of artificial intelligence algorithms in the nuclear field.