Research on the application of lacquer painting styletransfer recognition models in international arteducation

Amid growing demand for intelligent and cross-disciplinary educational technologies, this research addresses the integration ofcomputational intelligence into culturally grounded artistic instruction. In line with the journal’s focus on digital education andapplied computational sciences, this study explores a novel approach to style recognition through symbolic-neural models,aimed at enhancing interactive, learner-centric art education. Prior work in this domain largely relies on convolutional neuralnetworks trained on narrow datasets, often lacking semantic understanding of stylistic evolution and learner cognition, whichlimits their adaptability and depth. To overcome these limitations, we introduce a unified framework built upon the AestheticConcept Graph Transformer (ACGT) and the Curatorial Alignment Strategy (CAS). ACGT leverages symbolic-artistic graphstructures and multi-modal attention to model both perceptual cues and domain semantics, while CAS refines outputs throughdomain-specific constraints and learner-tailored optimization. This joint architecture excels in interpretive reasoning, styleclassification, and critique evaluation, ensuring pedagogical alignment and robust semantic integrity. Experimental resultsdemonstrate that the proposed model outperforms traditional baselines in both accuracy and interpretability, establishing astrong foundation for intelligent educational systems that incorporate symbolic reasoning, digital pedagogy, and human-mediainteraction in cross-cultural art contexts.