The construction of a digital dissemination platform for the intangible cultural heritage using convolutional neural network models
In order to promote the digital dissemination and preservation of Chinese intangible cultural heritage, this work constructs a digital platform for its transmission. The platform integrates a range of advanced technologies, including the Densely Connected Convolutional Networks - Bottleneck and Comp...
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-01-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S240584402417017X |
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| Summary: | In order to promote the digital dissemination and preservation of Chinese intangible cultural heritage, this work constructs a digital platform for its transmission. The platform integrates a range of advanced technologies, including the Densely Connected Convolutional Networks - Bottleneck and Compression model, a notable convolutional neural network, along with natural language processing algorithms, generative adversarial network algorithms, and neural collaborative filtering algorithms. The platform is validated with 224,055 publicly archived valid data records, ensuring its effectiveness and reliability. The results indicate that the heteroscedasticity-robust standard error is 0.15, and the platform achieves accuracies of 0.87 and 0.89 in 5-fold and 10-fold cross-validation, respectively, demonstrating the model's stability and predictive accuracy. Under various operating conditions, the platform's accuracy, recall, and F1-Score all exceed 0.85, with a root mean square error below 0.12, ensuring efficient and reliable recommendation performance. Regarding user experience, page load times remain steady between 6.45 and 10.25 s, with bounce rates and error rates controlled between 40.88 % to 58.33 % and 0.05 %–0.18 %, respectively. A/B testing and heatmap analysis indicate that optimizing page layout significantly improves click-through and conversion rates. Additionally, in the fields of oral literature and traditional medicine, the platform achieves entity recognition accuracies of 90.2 % and 91.3 %, respectively. The platform also demonstrates strong coverage in the knowledge of folklore and traditional crafts, with rates reaching 92.5 % and 89.8 %, respectively, showcasing its advantages across different domains. This work aims to integrate cultural heritage conservation, cultural inheritance, and digital dissemination, fostering the broad transmission and preservation of intangible cultural heritage in the era of big data. |
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| ISSN: | 2405-8440 |