This paper proposes a Physics-Informed Neural Network (PINN) based on the scattering equivalent source method. Through unsupervised learning, it addresses frequency-domain electromagnetic problems on two-dimensional metal boundaries, demonstrating a certain level of generalization. The introduced PINN leverages the equivalent source principle to represent metal boundaries effectively. Additionally, an attention mechanism is employed to establish correlations between different equivalent source sampling points, enabling dynamic input-output relationships.