Paper Title: AI-based tourist behavior analysis and cultural communication optimization strategies for Shanxi great wall heritage site
Authors: Xuehe Hou, Zulhilmi B Paidi
Corresponding Author: Zulhilmi B Paidi (zulsusila@gmail.com)/Malaysia
Abstract
This study analyses tourist behavior and cultural communication optimization strategies of the Shanxi Great Wall heritage site using more sophisticated artificial intelligence technologies. The gaps in heritage tourism are approached by applying machine learning, natural language processing, and multi-objective optimization to exhibit technological management while maintaining cultural integrity. Using a combination of qualitative and quantitative methods, this research gathered data from 1,200 tourists through surveys, interviews, and digital behavior observation as well as social media and online review analysis. Machine learning clustering analysis categorised tourists into five behavioral groups: Heritage Enthusiasts (28.7%), Cultural Explorers (23.4%), Adventure Seekers (19.8%), Quick Visitors (16.2%), and Social Influencers (11.9%). Each segment exhibited distinct engagement patterns and communication preferences. Random Forest outperformed in predicting satisfaction, achieving 87.3% accuracy, followed by Support Vector Machine (84.1%) and Neural Networks (82.6%). AI content optimization’s projected user engagement rate was 43.7% and cultural knowledge transfer effectiveness was improved by 52.1%. The rationalising optimization framework showed marked improvements on various business metrics such as an increase of 47.3% in satisfaction scores, 38.9% in cultural understanding, and a reduction of 29.6% in response times. Validation through pilot implementations proved the framework’s success in integrating conflicting goals of maximising visitor satisfaction, operational efficiency, and preserving cultural elements. This research adds to the growing literature on AI-powered management of heritage tourism and offers actionable recommendations for responsible cultural engagement at heritage sites around the world.
Keywords
Artificial Intelligence, Tourist Behavior Analysis, Cultural Heritage Tourism, Machine Learning, Optimization Strategies
