A painting art rendering system by deep learning framework and machine translation.

Under the context of educational informatization and cultural heritage, painting instruction in ethnic universities faces challenges such as difficulty in technique transmission, cross-linguistic barriers, and insufficient personalization. This study proposes a painting art rendering system based on deep learning and machine translation, establishing an integrated framework of "technique transmission - style rendering - cultural interpretation - personalized guidance." The system employs an improved generative adversarial network to achieve automatic rendering of eight ethnic painting styles and introduces a visual-context Transformer to accomplish semantic mapping of painting terminology across different ethnic languages. Validation was conducted on a multimodal dataset comprising 12,000 artworks and 5,000 terminology entries. Results showed that the style rendering module achieved an F1 score of 92.3%, representing an 8.7% improvement over traditional models. Meanwhile, the terminology mapping module reached a semantic matching rate of 89.6%, an increase of 6.2%. Ablation experiments indicated that the collaborative operation of the two modules enhanced overall performance by 11.5%. Teaching experiments showed that students using the system improved by 18.4%, 25.4%, and 17.6% in technique mastery, cultural understanding, and creative innovation, respectively, significantly outperforming the traditional approach. The study makes a contribution by proposing a collaborative teaching framework, introducing innovative modules for intelligent rendering and cross-linguistic interpretation, and empirically validating their educational value. This study provides a practical approach for the digital preservation of ethnic painting techniques and for facilitating cross-cultural communication.