Machine Learning Advances in Technology Applications: Cultural Heritage Tourism Trends in Experience Design

This study investigates the evolving trends in cultural heritage tourism experience design and examines how machine learning technologies are being applied to enhance visitor engagement and heritage preservation. Using bibliometric data from the Web of Science (WoS) and visualization tools such as VOSviewer, the research identifies key themes, author collaborations, and keyword clusters from 2016 to 2025. The analysis reveals a shift in focus from traditional conservation and display methods to user-centered experiences supported by advanced technologies. Machine learning techniques—such as deep learning, natural language processing, and multimodal data fusion—are increasingly used to personalize tours, analyze tourist behavior, restore damaged artifacts, and improve decision-making in resource management. Tools like CNNs and BERT models enable smart guiding systems and interactive Q&A features, while sentiment analysis enhances feedback mechanisms. The study also highlights several ongoing challenges, including data privacy issues, algorithmic bias, and unequal access to technological infrastructure, especially in developing regions. Ethical considerations and the need for human-centered design principles are emphasized to ensure that technological innovation aligns with cultural values and sustainability goals. In conclusion, this research provides a comprehensive overview of academic progress in cultural heritage tourism and illustrates the growing importance of AI and machine learning in creating immersive, efficient, and culturally respectful tourism experiences. The findings offer practical insights for scholars, heritage site managers, and policymakers seeking to leverage digital tools for both preservation and enhanced visitor satisfaction.

@article{Deng2025,
  title     = {Machine Learning Advances in Technology Applications: Cultural Heritage Tourism Trends in Experience Design},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {4},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Meihua Deng},
  doi       = {10.14569/IJACSA.2025.0160420},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160420}
}