<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The digitized quality of rural traditional cultural resources is relatively low, and there are problems such as heterogeneity and incompleteness in the resource data, resulting in the limitation of related cultural data information mining and the inability to realize deep development and innovation. Therefore, this paper combines the fully connected neural network and fuzzy C-mean clustering algorithm to construct a cultural digital resource clustering model, and based on the clustering results combined with CR-LDA and collaborative filtering algorithm to achieve personalized recommendation of rural traditional cultural digital resources. The experimental results show that the clustering model combining a fully connected neural network and a fuzzy C-mean clustering algorithm has a better clustering effect than the other three clustering models, and it also shows good robustness and stability. In addition, although the collaborative filtering model combining the CR-LDA algorithm has a slight increase in runtime compared to the LDA model runtime, the classification accuracy is significantly improved. It thus can provide platform users with practical and reliable cultural resource recommendations. Most users indicated in the survey that they agreed with the results of the model’s personalized cultural digital resource recommendations.</p>
</abstract>

The digitized quality of rural traditional cultural resources is relatively low, and there are problems such as heterogeneity and incompleteness in the resource data, resulting in the limitation of related cultural data information mining and the inability to realize deep development and innovation. Therefore, this paper combines the fully connected neural network and fuzzy C-mean clustering algorithm to construct a cultural digital resource clustering model, and based on the clustering results combined with CR-LDA and collaborative filtering algorithm to achieve personalized recommendation of rural traditional cultural digital resources. The experimental results show that the clustering model combining a fully connected neural network and a fuzzy C-mean clustering algorithm has a better clustering effect than the other three clustering models, and it also shows good robustness and stability. In addition, although the collaborative filtering model combining the CR-LDA algorithm has a slight increase in runtime compared to the LDA model runtime, the classification accuracy is significantly improved. It thus can provide platform users with practical and reliable cultural resource recommendations. Most users indicated in the survey that they agreed with the results of the model’s personalized cultural digital resource recommendations.

The Path of Digital Protection and Innovative Development of Rural Traditional Cultural Resources Supported by Intelligent Information

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

The digitized quality of rural traditional cultural resources is relatively low, and there are problems such as heterogeneity and incompleteness in the...

The Path of Digital Protection and Innovative Development of Rural Traditional Cultural Resources Supported by Intelligent Information

Online veröffentlicht: 15. Mai 2024

Seitenbereich: -

Eingereicht: 01. Feb. 2024

Akzeptiert: 12. Apr. 2024

DOI: https://doi.org/10.2478/amns-2024-1070

Schlüsselwörter
Intelligent information, Rural traditional culture, Digitization, Traditional cultural resources protection, Innovative development

© 2024 Qiaowei Yang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

The Path of Digital Protection and Innovative Development of Rural Traditional Cultural Resources Supported by Intelligent Information

Online veröffentlicht: 15. Mai 2024

Seitenbereich: -

Eingereicht: 01. Feb. 2024

Akzeptiert: 12. Apr. 2024

DOI: https://doi.org/10.2478/amns-2024-1070

SchlüsselwörterIntelligent information, Rural traditional culture, Digitization, Traditional cultural resources protection, Innovative development

© 2024 Qiaowei Yang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Intelligent information, Rural traditional culture, Digitization, Traditional cultural resources protection, Innovative development