Spiking Neural Networks have drawn much attention for their potential deployment in low computing power scenarios and interdisciplinary research. This paper focuses on a novel task of classifying Chinese Calligraphy styles properly and introduces a cutting-edge network called CaStySNN. Compared to same-structured traditional artificial neural networks, CaStySNN requires significantly less computing power, while demonstrating superior performances across different datasets. In the future, this innovative approach can be applied to neuromorphic devices, offering solutions to a wide range of challenges in the realms of fine arts and philology.