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

<p>In recent years, along with the rapid development of artificial intelligence, big data and social media, informatization in the tourism industry also shows an explosive trend. This paper constructs a tourism path planning system based on data mining technology and the selection method of the optimal path. The GS algorithm is used to optimize the SVM algorithm to form the GS-SVM fusion algorithm, which makes the tourism path planning and predicts the optimal path according to the specific conditions of the journey, the characteristics of the scenic spot itself, and the tourists’ needs. After testing, this system has a good prediction performance on the traffic accessibility, attraction congestion and crowd change of scenic tour path. It is found that the transportation accessibility of scenic tour paths is positively correlated with tourists’ experience. In addition, in the experiment on the advantages and disadvantages of tourism paths, the passage time of paths 14, 15 and 16 is more than 3 minutes. Still, the actual length of these three paths is not more than 350m, which indicates that there are things for tourists to visit and experience on the passage paths, thus lengthening the passage time. This shows that the system provides real-time and reference paths for tourists by mining social media sharing data.</p>
</abstract>

In recent years, along with the rapid development of artificial intelligence, big data and social media, informatization in the tourism industry also shows an explosive trend. This paper constructs a tourism path planning system based on data mining technology and the selection method of the optimal path. The GS algorithm is used to optimize the SVM algorithm to form the GS-SVM fusion algorithm, which makes the tourism path planning and predicts the optimal path according to the specific conditions of the journey, the characteristics of the scenic spot itself, and the tourists’ needs. After testing, this system has a good prediction performance on the traffic accessibility, attraction congestion and crowd change of scenic tour path. It is found that the transportation accessibility of scenic tour paths is positively correlated with tourists’ experience. In addition, in the experiment on the advantages and disadvantages of tourism paths, the passage time of paths 14, 15 and 16 is more than 3 minutes. Still, the actual length of these three paths is not more than 350m, which indicates that there are things for tourists to visit and experience on the passage paths, thus lengthening the passage time. This shows that the system provides real-time and reference paths for tourists by mining social media sharing data.

Design and Planning of Tourism Path Based on Social Media Sharing Data Mining

Applied Mathematics and Nonlinear Sciences

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

{"article-title":"Design and Planning of Tourism Path Based on Social Media Sharing Data Mining"}

In recent years, along with the rapid development of artificial intelligence, big data and social media, informatization in the tourism industry also shows an...

Design and Planning of Tourism Path Based on Social Media Sharing Data Mining

Pubblicato online: 26 feb 2024

Pagine: -

Ricevuto: 12 gen 2024

Accettato: 21 gen 2024

DOI: https://doi.org/10.2478/amns-2024-0544

Parole chiave
GS, SVM, Data mining, Path planning

© 2023 Meizhong Huang et al., published by Sciendo

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

Design and Planning of Tourism Path Based on Social Media Sharing Data Mining

Pubblicato online: 26 feb 2024

Pagine: -

Ricevuto: 12 gen 2024

Accettato: 21 gen 2024

DOI: https://doi.org/10.2478/amns-2024-0544

Parole chiaveGS, SVM, Data mining, Path planning

© 2023 Meizhong Huang et al., published by Sciendo

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

Parole chiave
GS, SVM, Data mining, Path planning