<p>Shui Script character recognition significantly contribute to the exploration of the abundant historical information embedded within it. However, the existing methods can only recognize a limited range of Shui Script characters. In this paper, we propose a framework that integrates a Bi-Correlational Attention module, combining Cross-Correlational and Self-Correlational Attention to align stroke position relationships between the support and query sets. Furthermore, we introduce a Dual Similarity Relation Module, designed to enhance discriminative feature representation through its Spatial Awareness Relation Module, thereby improving sensitivity to character stroke positions. To further refine feature extraction, we utilize a cosine similarity measurement based on local representation, effectively capturing fine-grained stroke features in Shui Script characters. Additionally, we construct Shui Script datasets S129 and SHD, further perform extensive experiments to validate the proposed method. Experimental results demonstrate that our approach outperforms existing few-shot learning methods in Shui Script recognition, achieving state-of-the-art performance.</p>