Taking the Nanling Mountainous Region of Hunan Province (Yuechengling, Dupangling, Mengzhuling, Qitianling) as the research site, a web crawler is made based on Python, the pictures posted by hikers in this area on 2bulu.com are extracted, and a total of 7 857 valid tagged photos from 2016 to 2020 were obtained. The computer deep learning algorithm is used to analyze the image representation content, and the analysis result and its corresponding information are imported into the Nvivo 12.0 qualitative analysis software for machine coding, the image elements are classified and integrated, and the image content analysis method combined with grounded theory is used to present the annotated photos. The landscape is classified again, high frequency words are extracted, and node aggregation is used to analyze hikers’preference for close-range labeling of Nanling Mountains in Hunan Province. The results show that : Outdoor travelers in Dupangling, Yuechengling, Mengzhuling and Qitianling in the southern Hunan region of Nanling Mountains generally pay more attention to the near-view labeling elements as mountain landscapes, and natural elements such as canyons and streams are more concerned. However, the low level of attention to plants as the main part of the forest may be related to the level of basic biological knowledge of outdoor hikers; different areas show certain differences in the content reflected by the close-range annotation pictures, and these differences are due to the main hiking routes passing by. The natural geographical environment, highly recognizable geographical indications, and local development and management measures are different; the close-range attention objects in the same area gradually become richer with the development of time sequence, and the close-range label elements that have appeared will gradually cover all element types in the area. Therefore, the number of newly emerging close-range annotation elements will become less and less, which means that the preference similarity of travelers' close-range annotations will become higher and higher; between different years, due to differences in management measures, development and construction, and national policies, travelers. The preference for close-range labeling will vary.