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

<p>This paper analyzes the power mechanism of tourism to help rural revitalization by elaborating the core content of rural tourism and rural revitalization. It also combines the advantages of the Lasso regression method and ridge regression method to form a flexible regression network model, which is used to predict the amount of tourists and tourists’ demand for rural tourism so as to put forward the implementation path of the tourism industry to help rural revitalization development. To verify the effectiveness of the proposed algorithm, data analysis is carried out in rural areas of L city as an example. The results show that compared with the Lasso regression model, the average absolute percentage error decreased by 2.41%, the average absolute error decreased by 2,233, and the root-mean-square error decreased by 2,967, which indicates that the elasticity regression network prediction model has a stronger generalization ability and a better prediction ability, and it can predict the number of tourists more accurately, and it can provide data for the tourism industry to contribute to rural revitalization and development. Reference.</p>
</abstract>

This paper analyzes the power mechanism of tourism to help rural revitalization by elaborating the core content of rural tourism and rural revitalization. It also combines the advantages of the Lasso regression method and ridge regression method to form a flexible regression network model, which is used to predict the amount of tourists and tourists’ demand for rural tourism so as to put forward the implementation path of the tourism industry to help rural revitalization development. To verify the effectiveness of the proposed algorithm, data analysis is carried out in rural areas of L city as an example. The results show that compared with the Lasso regression model, the average absolute percentage error decreased by 2.41%, the average absolute error decreased by 2,233, and the root-mean-square error decreased by 2,967, which indicates that the elasticity regression network prediction model has a stronger generalization ability and a better prediction ability, and it can predict the number of tourists more accurately, and it can provide data for the tourism industry to contribute to rural revitalization and development. Reference.

Research on the Implementation Path of Tourism Contributing to the Development of Rural Revitalization Combined with Elastic Regression Network Algorithm

Fuzhou University of International Studies and Trade

Applied Mathematics and Nonlinear Sciences

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

This paper analyzes the power mechanism of tourism to help rural revitalization by elaborating the core content of rural tourism and rural revitalization. It...

Research on the Implementation Path of Tourism Contributing to the Development of Rural Revitalization Combined with Elastic Regression Network Algorithm

Data publikacji: 29 lis 2023

Zakres stron: -

Otrzymano: 27 sty 2023

Przyjęty: 09 cze 2023

DOI: https://doi.org/10.2478/amns.2023.2.01295

Słowa kluczowe
Elastic regression network, Lasso regression, Ridge regression, Tourism industry, Rural revitalization

© 2023 Qin Chen, published by Sciendo

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

Research on the Implementation Path of Tourism Contributing to the Development of Rural Revitalization Combined with Elastic Regression Network Algorithm

Data publikacji: 29 lis 2023

Zakres stron: -

Otrzymano: 27 sty 2023

Przyjęty: 09 cze 2023

DOI: https://doi.org/10.2478/amns.2023.2.01295

Słowa kluczoweElastic regression network, Lasso regression, Ridge regression, Tourism industry, Rural revitalization

© 2023 Qin Chen, published by Sciendo

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

Słowa kluczowe
Elastic regression network, Lasso regression, Ridge regression, Tourism industry, Rural revitalization