What does this research mean for the field?

A multi-model fusion analysis method combining Bi-LSTM, LDA, and spatiotemporal mapping effectively identifies tourist needs and segments behavior patterns with an 82% prediction accuracy. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

This research aims to enhance understanding of tourist behavior through multi-cluster analysis to inform management strategies for scenic areas.

May 16, 2026International Journal of Information and Communication Technology0 citationsOpen Access

Multi-cluster data mining and analysis of tourist behaviour patterns for scenic area management

WQWei Qian

Key Points

This research aims to enhance understanding of tourist behavior through multi-cluster analysis to inform management strategies for scenic areas.
Bidirectional long short-term memory (Bi-LSTM) network analyzes review text semantics.
Latent Dirichlet allocation (LDA) identifies core topics from tourist feedback.
Multiple regression models assess diverse group preferences based on identified themes.
Five themes from positive and negative comments effectively reflect tourist feedback dimensions.
Specific group preferences: architectural features (preference coefficient 1.820) and affordability (preference coefficient 2.186).
Model achieves an overall prediction accuracy of 0.82.

Abstract

To address the fragmentation in tourist need identification and the disconnect between a multi-model fusion analysis method is proposed.This approach uses a bidirectional long short-term memory (Bi-LSTM) network to extract semantics from review texts and a latent Dirichlet allocation (LDA) model to identify core topics.A spatiotemporal cube structure maps emotional labels to spatiotemporal coordinates, quantifying experiential differences and optimising tourist group segmentation.Experimental results showed that five themes from both positive and negative comments and five from negative comments were well-separated, effectively reflecting dimensional differences in tourist feedback.Multiple regression models indicated varied group preferences, with one group favouring architectural features (preference coefficient of 1.820) and another prioritising affordability (preference coefficient of 2.186).The overall prediction accuracy of the model is 0.82.The research results provide data-driven decision-making basis for precise service design and resource optimisation allocation in scenic spots.

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