<p>Oracle Bone Script, as the earliest known form of Chinese writing, plays a significant role in archaeological and historical studies due to the importance of recognizing its imagery. However, existing deep learning technologies face challenges in automatically recognizing Oracle Bone Script, including the lack of fine control over local features, the neglect of texture information, and insufficient learning of highly discriminative features. To address these issues, this paper introduces a novel image processing model for Oracle Bone Script named OracleNet. OracleNet consists of an Adaptive Deformation Module, a Texture–Structure Decoupling Module, and a Multi-Level Structured Perceptual Attention Module. The Adaptive Deformation Module enhances local control through adaptive points, maintaining the semantic integrity of the script; the Texture–Structure Decoupling Module distinguishes between texture and structural elements, improving recognition accuracy; the Multi-Level Structured Perceptual Attention Module refines differences through macro and micro perspectives. OracleNet has been validated on multiple datasets, achieving state-of-the-art performance on the Oracle-241, OBC306 and Oracle-MNIST datasets, demonstrating the model’s superior accuracy and robustness.</p>