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

<p>Due to the improvement of national consumption levels, the Guizhou tourism industry plays an increasingly important role in the economy of Guizhou province, and the proportion of tourism revenue to GDP is increasing. We combine the improved FOA algorithm with an echo-state network, taking into account the characteristics of Guizhou tourism demand, to construct an AFOA-ESN prediction model for Guizhou tourism demand. The new algorithm fixes the problems with the old Drosophila algorithm by changing the number of Drosophila populations, the size of the search step, and the best place for the first iteration. This makes the new algorithm better at local searches and more efficient. We select the annual data on the number of travelers to Guizhou from ten provinces as the dataset and apply the simulation analysis method to test the effectiveness of the previously mentioned prediction model. The data show that among the ten sets of experiments, the AFOA-ESN prediction model has seven times the smallest MAPE value, while the AFOA-ESN model has six times the smallest MSE value. In addition, the consumption structure of Guizhou tourism tourists from 2010 to 2022 did not change much in this stage, showing the development of shock, sensitivity, and vulnerability. Based on the analysis results, we propose the tourism marketing strategy of “Ethnicity + Folklore + Folk Lodging.” This study selects forecast information suitable for the tourism industry and provides the necessary reference for decision-making in tourism-related departments.</p>
</abstract>

Due to the improvement of national consumption levels, the Guizhou tourism industry plays an increasingly important role in the economy of Guizhou province, and the proportion of tourism revenue to GDP is increasing. We combine the improved FOA algorithm with an echo-state network, taking into account the characteristics of Guizhou tourism demand, to construct an AFOA-ESN prediction model for Guizhou tourism demand. The new algorithm fixes the problems with the old Drosophila algorithm by changing the number of Drosophila populations, the size of the search step, and the best place for the first iteration. This makes the new algorithm better at local searches and more efficient. We select the annual data on the number of travelers to Guizhou from ten provinces as the dataset and apply the simulation analysis method to test the effectiveness of the previously mentioned prediction model. The data show that among the ten sets of experiments, the AFOA-ESN prediction model has seven times the smallest MAPE value, while the AFOA-ESN model has six times the smallest MSE value. In addition, the consumption structure of Guizhou tourism tourists from 2010 to 2022 did not change much in this stage, showing the development of shock, sensitivity, and vulnerability. Based on the analysis results, we propose the tourism marketing strategy of “Ethnicity + Folklore + Folk Lodging.” This study selects forecast information suitable for the tourism industry and provides the necessary reference for decision-making in tourism-related departments.

An Exploration of Tourism Marketing Strategies of “Ethnicity + Folklore + Folk Lodging” in Guizhou Province Based on Echo State Network Modeling

Applied Mathematics and Nonlinear Sciences

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

Due to the improvement of national consumption levels, the Guizhou tourism industry plays an increasingly important role in the economy of Guizhou province,...

An Exploration of Tourism Marketing Strategies of “Ethnicity + Folklore + Folk Lodging” in Guizhou Province Based on Echo State Network Modeling

Data publikacji: 05 sie 2024

Zakres stron: -

Otrzymano: 04 kwi 2024

Przyjęty: 03 lip 2024

DOI: https://doi.org/10.2478/amns-2024-2202

Słowa kluczowe
Drosophila optimization algorithm, Echo state network, Prediction model, Tourism marketing strategy

© 2024 Ting Kang et al., published by Sciendo

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

An Exploration of Tourism Marketing Strategies of “Ethnicity + Folklore + Folk Lodging” in Guizhou Province Based on Echo State Network Modeling

Data publikacji: 05 sie 2024

Zakres stron: -

Otrzymano: 04 kwi 2024

Przyjęty: 03 lip 2024

DOI: https://doi.org/10.2478/amns-2024-2202

Słowa kluczoweDrosophila optimization algorithm, Echo state network, Prediction model, Tourism marketing strategy

© 2024 Ting Kang et al., published by Sciendo

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

Słowa kluczowe
Drosophila optimization algorithm, Echo state network, Prediction model, Tourism marketing strategy