Article: Big data intelligent analysis modelling for predicting tourist behaviour in heritage sites Journal: International Journal of Reasoning-based Intelligent Systems (IJRIS) 2026 Vol.18 No.11 pp.57 - 68 Abstract: Cultural heritage sites, as invaluable carriers of human civilisation, attract large numbers of visitors. Accurate prediction of visitor behaviour is crucial for effective site management. However, existing research struggles to fully capture the complex dynamic changes in visitor behaviour, resulting in suboptimal prediction accuracy. To address these challenges, this paper first analyses visitor travel preferences based on improved term frequency-inverse document frequency and hierarchical clustering. Then, a spatio-temporal multi-scale graph is constructed to characterise the dynamic evolution of visitor behaviour across temporal and spatial dimensions. Next, graph neural networks are employed to extract and fuse features from multidimensional behavioural preference data. Finally, the transformer captures key spatio-temporal factors to achieve precise visitor behaviour prediction. Experimental results demonstrate that the proposed model achieves a weighted F1-score at least 4.14% higher than baseline models, providing scientific decision support for efficient heritage site management. Inderscience Publishers - linking academia, business and industry through research

Title: Big data intelligent analysis modelling for predicting tourist behaviour in heritage sites

Authors: Huihui Luo

Addresses: School of Digital Culture and Tourism, Xianda College of Economics and Humanities, Shanghai International Studies University, Shanghai, 202162, China

Abstract: Cultural heritage sites, as invaluable carriers of human civilisation, attract large numbers of visitors. Accurate prediction of visitor behaviour is crucial for effective site management. However, existing research struggles to fully capture the complex dynamic changes in visitor behaviour, resulting in suboptimal prediction accuracy. To address these challenges, this paper first analyses visitor travel preferences based on improved term frequency-inverse document frequency and hierarchical clustering. Then, a spatio-temporal multi-scale graph is constructed to characterise the dynamic evolution of visitor behaviour across temporal and spatial dimensions. Next, graph neural networks are employed to extract and fuse features from multidimensional behavioural preference data. Finally, the transformer captures key spatio-temporal factors to achieve precise visitor behaviour prediction. Experimental results demonstrate that the proposed model achieves a weighted F1-score at least 4.14% higher than baseline models, providing scientific decision support for efficient heritage site management.

Keywords: heritage site; visitor behaviour prediction; big data analysis; graph neural network; transformer model.

DOI: 10.1504/IJRIS.2026.152723

International Journal of Reasoning-based Intelligent Systems, 2026 Vol.18 No.11, pp.57 - 68

Received: 03 Jan 2026
Accepted: 30 Jan 2026
Published online: 07 Apr 2026 *

Title: Big data intelligent analysis modelling for predicting tourist behaviour in heritage sites

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