Discovering the tourists' behaviors and perceptions in a tourism destination by analyzing photos' visual content with a computer deep learning model: The case of Beijing

Abstract Visual content analysis of tourist photos is an effective way to excavate tourist behavior and explore tourists' cognition in the tourism destination. With the development of computer deep learning and big data mining technology, identifying the content of massive numbers of tourist photos by Artificial Intelligence (AI) approaches breaks through the limitations of manual approaches of identifying photos' visual information, e.g. small sample size, complex identification process and results deviation. In this study, 35,356 Flickr tourists' photos in Beijing were identified into 103 scenes by computer deep learning technology. Comparison through statistical analysis for behaviors and perceptions of tourists from different continents and countries was conducted. Tourists' cognitive maps with different perceptual themes were visualized according to photos' geographical information by ArcGIS. The field of how to apply AI technology into tourism destination research was explored and extended by this trial study.