非遗文化虚拟体验的互动设计与推广

IF 3.1 Q1 Mathematics Applied Mathematics and Nonlinear Sciences Pub Date : 2024-01-01 DOI:10.2478/amns-2024-0838
Xi Zhao, Shenghua Duan
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引用次数: 0

摘要

在科技飞速发展的背景下,非遗文化的数字化转型意义重大,不仅有助于保护和传承,还能通过创新方式吸引更多受众。本研究探讨了非遗文化的虚拟体验交互设计与推广方法。研究利用深度学习超采样技术优化非遗手工艺的数字化活化和交互设计,提升用户体验,促进文化传播。经过有效筛选,实验期间共收集问卷 450 份,获得有效样本 400 份。利用深度学习技术优化非遗虚拟体验的交互设计,能显著提高用户对非遗文化的认知和兴趣。体验后,参与者对非遗文化内容认知的正确率平均提高到95%以上,其中对植物元素和排列形式认知的正确率高达98.9%。研究发现,社区互动功能在提高用户满意度方面发挥了重要作用。深度学习超采样技术对非遗文化虚拟交互设计具有实质性作用,可有效促进非遗文化的活态传承与推广。
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Interactive design and promotion of virtual experience of non-heritage culture
In the context of rapid technological development, the digital transformation of non-heritage culture is significant, which not only helps to protect and inherit, but also attracts more audiences through innovative ways. This study explores non-heritage cultures’ virtual experience interaction design and promotion methods. It investigates the optimization of digital revitalization and interaction design of non-heritage handicrafts with deep learning oversampling techniques to enhance user experience and promote cultural dissemination. After effective screening, 400 valid samples were obtained after collecting 450 questionnaires during the experiment. The interaction design of non-heritage virtual experiences optimized with deep learning technology can significantly improve users’ knowledge and interest in non-heritage culture. The correct rate of participants’ cognition of NRL cultural content increased to more than 95% on average after the experience, in which the proper rate of cognition of plant elements and arrangement forms was as high as 98.9%. The study found that the community interaction function plays a significant role in improving user satisfaction. Deep learning oversampling technology has a substantial effect on the virtual interaction design of non-heritage culture, which can effectively promote the living inheritance and promotion of non-heritage culture.
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来源期刊
Applied Mathematics and Nonlinear Sciences
Applied Mathematics and Nonlinear Sciences Engineering-Engineering (miscellaneous)
CiteScore
2.90
自引率
25.80%
发文量
203
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