Abstract Rural tourism has gradually become a popular form of tourism nowadays, and the implementation of precision marketing according to the preferences and needs of different travelers is one of the directions to enhance the competitiveness of rural tourism brand. This paper explores the path of cloud computing to promote the precise marketing of rural tourism, and improves the clustering center selection and computational complexity of K-means algorithm based on density method and trilateral relationship theory. The collaborative filtering algorithm is utilized to calculate the similarity between tourist attractions and user ratings to generate a recommendation list that can be used for attraction recommendation. It is proved by experiments that the clustering results of tourists in rural tourism can be divided into four types: personalized push-sensitive, social media and self-media active access to information, technological interactive experience, and dependence on the travel path customization + positioning push project type, etc. Tourism consumption data mining, integration of digital media marketing, and upgrading of the cloud-based big data tourism service information platform should be adopted to improve the marketing of rural tourism, respectively.
Sang et al. (Wed,) studied this question.
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