Understanding tourists' urban images from Big Data using convolutional neural networks

The Internet and social media have become important carriers of destination image dissemination, and the photos on social platforms reflect to a certain extent tourists' perception preferences in tourist destinations. This paper uses the photos actively uploaded by Xi'an tourists on the Liangbulu website as the research data source, and uses the convolutional neural network model to identify the photo scene, then analyzes the tourism image of Xi'an and its spatial distribution characteristics from the perspective of tourists' perception. The study shows that the spatial distribution of Xi'an's building, urban landscape, place/region, transportation, interpretation, night scenery, and water tourism image is generally "concentrated in the city center, sparse and isolated in the periphery"; the people and nature tourism images generally show the characteristics of "sparse and scattered" spatial distribution.