Protein spatial structure comparison plays a pivotal role in understanding biological functions, evolutionary relationships, and disease mechanisms. The recent advancements in AI-based prediction tools, such as AlphaFold, have led to the generation of an unprecedented number of protein structures, dramatically expanding structural databases. However, this exponential growth has exposed substantial computational bottlenecks in conventional comparison tools (e.g., CE, Dali), which suffer from poor scalability and efficiency. Even state-of-the-art approaches like ADAMS struggle to process large-scale, continuous structural data effectively. To this end, We propose PSCA, the FPGA-based accelerator specifically designed for protein structure comparison. By employing a non-stagnant sliding window, symmetry simplification and a series of dedicated hardware-optimized designs, we enhance the fluidity and parallelism of the ADAMS algorithm, achieving significant speedups in both descriptor database construction and matching phases. Experimental results conducted on a database comprising 12,414 human proteins and 15,059 C. elegans proteins demonstrated that PSCA achieves \(21.4\times - 29.9\times \) speedup in database construction and \(18\times - 265\times \) in matching process compared to the original Golden ADAMS software.