Affiliations: [a] College of Business, Yantai Nanshan University, Yantai, Shandong, China | [b] College of Applied Technology and Training, Yantai Nanshan University, Yantai, Shandong, China | [c] Educational Affairs Office, Yantai Nanshan University, Yantai, Shandong, China
Correspondence:
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Corresponding author: Zhe Zhang, College of Applied Technology and Training, Yantai Nanshan University, Yantai, Shandong 265700, China. E-mail: zhangzhe_vip@outlook.com.
Abstract: With the gradual urbanization of the countryside, people’s lifestyles have also undergone tremendous changes. Most people tend to live in the countryside, and people also choose tourist attractions with a rural flavor when traveling. This social background continues to promote the development of rural eco-tourism. develop. While the development of rural eco-tourism is driven by the dividends of the times, it may also be affected by various factors and lead to operational difficulties. In order to keep rural eco-tourism scenic spots in a profitable state, a comprehensive evaluation of rural eco-tourism has become the focus. The method of comprehensive evaluation of rural ecotourism is mainly completed by combining AI algorithms, big data and other auxiliary tools. However, due to the variety of evaluation-related factors involved in the comprehensive evaluation of rural ecotourism, the above evaluation methods cannot fully take into account the rural areas. All aspects of the comprehensive evaluation of ecotourism, so it is necessary to use a method that can complete an effective comprehensive evaluation with less evaluation-related factors to conduct a real evaluation of rural ecotourism. The grey clustering evaluation model is an evaluation method evolved on the basis of the research of Chinese scholars. It can classify and predict the categories according to some characteristics of the clustered objects, which effectively solves the inaccuracy and considerations existing in various current evaluation methods. Incomplete situation. This paper firstly sorts out the grey clustering method and related concepts and theories of rural ecotourism data. After a detailed analysis, and then combined with the gray clustering model to improve the algorithm, an evaluation method with better performance than the traditional gray clustering method was designed. It is found that the improved grey clustering method has more advantages than the method of AI algorithm, big data algorithm and other auxiliary tools for comprehensive evaluation of rural ecotourism.
