Multi-modal Synergy: Bridging Chinese Culture Expression and Teaching Interaction in Art English Textbooks via Self-supervised Learning

To address this, we propose a novel methodology integrating self-supervised learning with the LXMERT model. The CLIP model is used to align text, image, and video data in a shared semantic space.

Then, LXMERT's two-stream Transformer architecture extracts and fuses multimodal features, enhancing fusion consistency. A Chinese cultural knowledge graph boosts cultural concept semantic expression and improves textbook content structure. Our two-way interactive model and cross-cultural learning path optimization strategy also enhance teaching adaptability and communication effectiveness.