Research on somatic interactive exhibition design of lacquer artwork based on multi-source data analysis
The exhibition of lacquer art, as a unique expression of contemporary art and culture, still lacks a comprehensive and in-depth research framework both in theory and practice. In response, this paper proposes an innovative exhibition model centered on lacquer artworks, integrating modern information...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-12-01
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| Series: | Systems and Soft Computing |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772941925000869 |
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| Summary: | The exhibition of lacquer art, as a unique expression of contemporary art and culture, still lacks a comprehensive and in-depth research framework both in theory and practice. In response, this paper proposes an innovative exhibition model centered on lacquer artworks, integrating modern information technology to enable somatosensory engagement and interactive experiences. First, it explores the theoretical foundation of multi-source data and related algorithms, with a focus on ACS and ACO algorithms within neural networks, providing strong theoretical support for the model design. Then, a comparative analysis model of somatosensory interaction technology is developed based on communication chain relationships. The paper also examines the practicality and advantages of this technology in art exhibitions, offering robust technical support for the modernization of lacquer art displays. Finally, leveraging the powerful capabilities of deep neural networks, a synergistic model for multi-source data analysis is proposed and effectively applied to the Grand Lacquer Art Exhibition. Through the careful selection of relevant datasets for training, the model significantly enhances the harmony between the exhibited works and the audience, greatly improving both the formats and the impact of visual communication. It also delivers a deeper and more immersive artistic experience to viewers. The final evaluation shows that the model effectively addresses precision challenges caused by sparse data, maintaining an error margin within 3%, while successfully revealing the intricate connections between users and the artworks. |
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| ISSN: | 2772-9419 |