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

<p>In this paper, we apply FCM to data fusion and continuously iterate to create a data fusion model that achieves classification and fusion. The model has been improved to form the ARFCM data fusion model. Then, we select the evaluation indexes of urban tourism scenic spot management satisfaction and build the evaluation system of urban tourism scenic spot management satisfaction based on multiple data fusions. Finally, gender differences, age differences, importance, and other indicators of tourist satisfaction are selected to evaluate the management level of urban tourism scenic spots. The p-value of the t-test on staff service satisfaction is 0.008, the p-value of the t-test on scenic spot price satisfaction is 0.01, and the p-value of the t-test on scenic spot comprehensive service satisfaction is 0.03. This paper’s research provides strong support and a scientific basis for improving the management level of urban tourism scenic spots.</p>
</abstract>

In this paper, we apply FCM to data fusion and continuously iterate to create a data fusion model that achieves classification and fusion. The model has been improved to form the ARFCM data fusion model. Then, we select the evaluation indexes of urban tourism scenic spot management satisfaction and build the evaluation system of urban tourism scenic spot management satisfaction based on multiple data fusions. Finally, gender differences, age differences, importance, and other indicators of tourist satisfaction are selected to evaluate the management level of urban tourism scenic spots. The p-value of the t-test on staff service satisfaction is 0.008, the p-value of the t-test on scenic spot price satisfaction is 0.01, and the p-value of the t-test on scenic spot comprehensive service satisfaction is 0.03. This paper’s research provides strong support and a scientific basis for improving the management level of urban tourism scenic spots.

The Construction of Satisfaction Evaluation System of Urban Tourism Scenic Area Management Based on Multiple Data Fusion

Applied Mathematics and Nonlinear Sciences

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

In this paper, we apply FCM to data fusion and continuously iterate to create a data fusion model that achieves classification and fusion. The model has been...

The Construction of Satisfaction Evaluation System of Urban Tourism Scenic Area Management Based on Multiple Data Fusion

Pubblicato online: 30 ott 2023

Pagine: -

Ricevuto: 04 dic 2022

Accettato: 10 mag 2023

DOI: https://doi.org/10.2478/amns.2023.2.00885

Parole chiave
Multivariate data fusion, FCM algorithm, ARFCM algorithm, Variance analysis, Tourism scenic area management

© 2023 Gang Xu, published by Sciendo

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

The Construction of Satisfaction Evaluation System of Urban Tourism Scenic Area Management Based on Multiple Data Fusion

Pubblicato online: 30 ott 2023

Pagine: -

Ricevuto: 04 dic 2022

Accettato: 10 mag 2023

DOI: https://doi.org/10.2478/amns.2023.2.00885

Parole chiaveMultivariate data fusion, FCM algorithm, ARFCM algorithm, Variance analysis, Tourism scenic area management

© 2023 Gang Xu, published by Sciendo

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

Parole chiave
Multivariate data fusion, FCM algorithm, ARFCM algorithm, Variance analysis, Tourism scenic area management