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

<p>In the background of the intelligent era, combining big data technology to improve rural tourism landscape planning and scale management is an important means to promote rural transformation and development and farmers’ employment and income. Firstly, a clustering algorithm is proposed to be applied to rural tourism landscape planning based on big data, and the clustering algorithm mainly consists of heat measurement, spatial analysis method, kernel density estimation and hot spot identification method. Then, based on the current situation of rural tourism landscape development in China, the rural tourism network structure evaluation index system is constructed, and the evaluation index system consists of network density, central potential, average path, and clustering coefficient. Finally, to verify the accurate prediction performance of the method in this paper, rural tourism in Guizhou province is selected as the research object, and the experiment consists of 100 statistical iterations of the accurate prediction performance of the two methods. The results show that the method of this paper: with the increase of the number of iterations, the prediction accuracy increases from the initial 78.62 to 99.3%, the average prediction accuracy is 93.56%, and the accuracy of rural tourism passenger flow demand prediction by the method of this paper is higher. This study guides the efficient flow of rural tourism and promotes the high-quality development of rural tourism, which is of great significance to the regional cooperation and coordinated development of rural tourism, spatial optimization and traffic diversion, planning and construction and marketing.</p>
</abstract>

In the background of the intelligent era, combining big data technology to improve rural tourism landscape planning and scale management is an important means to promote rural transformation and development and farmers’ employment and income. Firstly, a clustering algorithm is proposed to be applied to rural tourism landscape planning based on big data, and the clustering algorithm mainly consists of heat measurement, spatial analysis method, kernel density estimation and hot spot identification method. Then, based on the current situation of rural tourism landscape development in China, the rural tourism network structure evaluation index system is constructed, and the evaluation index system consists of network density, central potential, average path, and clustering coefficient. Finally, to verify the accurate prediction performance of the method in this paper, rural tourism in Guizhou province is selected as the research object, and the experiment consists of 100 statistical iterations of the accurate prediction performance of the two methods. The results show that the method of this paper: with the increase of the number of iterations, the prediction accuracy increases from the initial 78.62 to 99.3%, the average prediction accuracy is 93.56%, and the accuracy of rural tourism passenger flow demand prediction by the method of this paper is higher. This study guides the efficient flow of rural tourism and promotes the high-quality development of rural tourism, which is of great significance to the regional cooperation and coordinated development of rural tourism, spatial optimization and traffic diversion, planning and construction and marketing.

The application of big data technology in rural tourism landscape planning under the background of the intelligent era

Applied Mathematics and Nonlinear Sciences

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

In the background of the intelligent era, combining big data technology to improve rural tourism landscape planning and scale management is an important means...

The application of big data technology in rural tourism landscape planning under the background of the intelligent era

Data publikacji: 04 wrz 2023

Zakres stron: -

Otrzymano: 25 lis 2022

Przyjęty: 15 mar 2022

DOI: https://doi.org/10.2478/amns.2023.2.00283

Słowa kluczowe
Big data technology, Clustering algorithm, Rural tourism landscape, Evaluation index system, Prediction accuracy performance

© 2023 Lexu Chen, published by Sciendo

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

The application of big data technology in rural tourism landscape planning under the background of the intelligent era

Data publikacji: 04 wrz 2023

Zakres stron: -

Otrzymano: 25 lis 2022

Przyjęty: 15 mar 2022

DOI: https://doi.org/10.2478/amns.2023.2.00283

Słowa kluczoweBig data technology, Clustering algorithm, Rural tourism landscape, Evaluation index system, Prediction accuracy performance

© 2023 Lexu Chen, published by Sciendo

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

Słowa kluczowe
Big data technology, Clustering algorithm, Rural tourism landscape, Evaluation index system, Prediction accuracy performance