Research on Rural Tourism Feature Segmentation Method Based on Hierarchical Cluster Analysis

Wu Jun

2025

Abstract

The feature division method plays an important role in rural tourism, but there is the problem of inaccurate division and positioning. The traditional deep learning algorithm cannot solve the problem of dividing characteristics in rural tourism, and the effect is not satisfactory. With the continuous development of modern tourism, people's demand for tourism is gradually diversified and personalized. In particular, rural tourism, with its unique natural scenery, traditional culture and pastoral life experience, has become the first choice for more and more urban residents for leisure and vacation. However, due to the diversity and complexity of resources in rural areas, how to scientifically and effectively develop and manage tourism resources and enhance the attractiveness and competitiveness of rural tourism has become the focus of attention in the industry. In this context, hierarchical cluster analysis, as an effective data mining technique, provides a new perspective and method for the characteristic division of rural tourism..

Paper Citation

in Harvard Style

Jun W. (2025). Research on Rural Tourism Feature Segmentation Method Based on Hierarchical Cluster Analysis. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 1: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 182-187. DOI: 10.5220/0013538000004664

in Bibtex Style

@conference{incoft25,
author={Wu Jun},
title={Research on Rural Tourism Feature Segmentation Method Based on Hierarchical Cluster Analysis},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 1: INCOFT},
year={2025},
pages={182-187},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013538000004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 1: INCOFT
TI - Research on Rural Tourism Feature Segmentation Method Based on Hierarchical Cluster Analysis
SN - 978-989-758-763-4
AU - Jun W.
PY - 2025
SP - 182
EP - 187
DO - 10.5220/0013538000004664
PB - SciTePress