Research progress on the application of artificial intelligence in ecotourism monitoring and management in natural protected areas

Objectives This study employed a combined approach of bibliometric analysis and content analysis to systematically review the research trajectory and development trends of artificial intelligence (AI) in the ecological recreation monitoring and management of natural protected areas, examining dimensions such as technical pathways, application domains, research themes, and frontier hotspots. The aim is to provide theoretical support and practical insights for the technological integration and management model innovation of AI in the monitoring and management of natural protected areas.

Methods Based on relevant literature from the Web of Science and CNKI databases spanning 2005 to 2024, CiteSpace software was utilized for visual analysis and comparative examination of domestic and international research trends.

Results The results showed that: 1) Research on AI in ecotourism monitoring and management of natural protected areas exhibited an exponential growth trend, which can be categorized into three phases: an exploratory period characterized by nascent technology and auxiliary supervision (2005–2016), a developmental period marked by technological accumulation and precision enhancement (2017–2020), and a maturation period defined by technology integration and deepened cognition (2021–present). 2) The application of AI in natural protected area monitoring and management was concentrated in three major directions: visitor flow management, visitor behavior recognition and typology classification, and assessment of ecological suitability and carrying capacity. 3) Differences existed between domestic and international research in terms of methodological approaches and thematic content. Domestic research focused on the impact on the ecological environment and adopted a combination of quantitative and qualitative research methods, while international research places greater emphasis on optimizing recreational experiences and mainly use quantitative research methods such as modeling.

Conclusions Future research on AI in ecotourism monitoring and management of natural protected areas should expand to encompass diverse types of natural protected areas, strengthen the construction of theoretical models, and promote the integration of theory and practice in the development of intelligent natural protected area systems.