In 1992, the China National Tourism Administration (CNTA) listed “ice-snow scenery tours” as one of the 14 special tourism products in China for the first time. In the conclusion, strategies are put forward to improve image perceptions of ice-snow tourism and promote the sustainable development of ice and snow tourism. (3) Tourists spoke highly of overall images of ice-snow tourism in Jilin Province but few were willing to revisit. Meanwhile, passive emotional expressions accounted for 21.07% and tourists gave passive evaluations for travel, transportation, accommodation and catering. Tourists gave a positive evaluation for Changbai Mountain, the snow landscape of Tianchi and skiing facilities. “Changbai Mountain” and “rime” are the core words, that is, tourists are most impressed by the scenic spot and landscape of “Changbai Mountain and rime.” (2) Positive emotional expressions accounted for 67.23% of perceptions of images of ice-snow tourism. The frequency of tourism attractions is the highest, followed by tourism facilities and the tourism service environment. The study found that: (1) Perceptions of images of ice-snow tourism can be divided into five categories: tourism attractions, tourism activities, tourism facilities, tourism features and the tourism service environment. In this paper, python programming was used to crawl online travel journals and reviews about Jilin province’s ice-snow tourism on the Internet to analyze the frequency of frequently used words, their classification, word cloud and co-occurrence network, and other aspects of image perception, and proceed to the emotional perception of and emotional attitude to the emotional images and an overall image analysis. Ice-snow tourism is a hot topic nowadays, and research on perceptions of images of ice-snow tourism has become a focus. Destination image perception as a branch of destination image research is of great significance to tourists’ decision-making and destination image building. The analysis with Python technology, including optimized artificial intelligence technology, is designed on the basis of symmetry principles. Symmetries play very important roles in the analysis of cognitive and emotional attitudes.
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