Journal of Applied Science and Engineering

Published by Tamkang University Press

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Research on fuzzy clustering recommendation system for intelligent tourism management

 2026

 Volume 34

Luxi Chen1 and Yanna Huang2

1School of History and Law, Yulin Normal University, Yulin, Guangxi 537000, China

2Student Affairs Office, Yulin Normal University, Yulin, Guangxi 537000, China

Received: May 09, 2026
Accepted: June 26, 2026
Publication Date: August 09, 2026

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Proposed Intelligent Fuzzy Clustering Framework for Tourism Data Analysis. 

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

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Tourism analytics is essential for understanding traveler behavior and supporting effective destination management. This study proposes a hybrid framework that combines deep learning, evolutionary optimization, and fuzzy clustering to improve tourism data segmentation. A realworld tourism dataset is preprocessed using Min-Max normalization and one-hot encoding. A Variational Autoencoder (VAE) is employed to extract compact latent features from high dimensional data, while Reinforced Evolution Strategy (RES) selects the most informative features by optimizing information gain and reconstruction quality. Fuzzy C-Means (FCM) clustering is then applied to categorize tourism records while handling uncertainty through soft cluster mem berships. The clustering performance is assessed using Partition Coefficient (PC), Partition Entropy (PE), and Xie-Beni Index (XB). Results demonstrate effective cluster formation, achieving a PC of 0.65, PE of 0.68, and clear cluster separation. The proposed framework also demonstrated strong destination retrieval and ranking capability, achieving Precision@10 of 0.89 , Recall@10 of 0.86 , F1-Score of 0.88 , MAP of 0.91 , and NDCG of 0.93. Compared to previous works, the model showed improved compactness and reduced ambiguity in cluster membership.

Keywords: Tourism Recommendation, Variational Autoencoder, Reinforced Evolution Strategy, Fuzzy C-Means Clustering, Clustering Evaluation.

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