聚类分析与判别分析在智慧旅游中的应用与探索
The Application and Exploration of Cluster Analysis and Discriminant Analysis in the Smart Tourism

近年来，我国旅游业一直保持着高速稳定的发展趋势。在近两年疫情的影响下，旅游业发展受到影响，但我国旅游业目前仍处于持续发展增长的时期。并且，随着信息技术的持续发展，大数据时代已经悄然来临，社会各产业与大数据技术进行了深度的融合，智慧旅游应运而生。由于全国各地区之间差异等各种因素的影响，各个地区的旅游业发展水平呈现出不一致性。本文从Tableau可视化以及智慧旅游概念出发，利用Tableau作图工具对全国近十年的旅游发展趋势进行分析。由于影响智慧旅游发展水平的指标有很多，本文选取2019年全国各省份的具有代表性的若干指标进行研究，即选取旅游总收入、总人次、旅游类居民消费价格指数等9个指标。对于各省份智慧旅游的发展，利用聚类分析和判别分析模型对全国各省份的智慧旅游发展现状进行分析。最后，利用多元统计分析软件SPSS得到分析的结果，将各省份归为不同的类别，寻找原因并给出相应的对策。经研究可得，将全国各省份归为5类。结合各地区智慧旅游的发展现状，给出相应的建议和对策。对于北京、上海发展较成熟的地区，应更加注重高级智能化的旅游产品；对于贵州，由于地区、环境等的原因导致现阶段该区域的智慧旅游相对落后，政府的相关策略应向该地区偏斜。
In recent years, China’s tourism industry has maintained a high-speed and stable development trend. Under the influence of the epidemic in the past two years, the development of tourism has been affected, but China’s tourism industry is still in a period of development and growth. In ad-dition, with the continuous development of information technology, the era of big data has qui-etly come, and various social industries and big data technology have been deeply integrated, then smart tourism has emerged at the historic moment. Due to the influence of various factors such as differences among different regions in the country, the level of tourism development in different regions is inconsistent. Starting with the Tableau visualization and the concept of smart tourism, this paper uses the Tableau mapping tool to analyze the development trend of national tourism in the past decade. As there are many indicators affecting the development level of smart tourism, this paper selects several representative indicators of various provinces in 2019, that is, nine indicators such as total tourism revenue, total person-time and tourism consumer price index, etc. For the development of smart tourism in each province, the cluster analysis and discriminant analysis model are used to analyze the development status of smart tourism in each province. Finally, the analyzed results are obtained by using the software SPSS to perform multivariate statistical analysis, classifying the provinces into different categories, finding the reasons and giving corresponding countermeasures. After the study, the provinces in the coun-try were classified into five categories. Combined with the development status of smart tourism in each region, the corresponding suggestions and countermeasures are given. For Beijing and Shanghai, more attention should be paid to advanced and intelligent tourism products; For Guizhou, the smart tourism in this region is relatively backward at present due to regional and environmental reasons, and the government’s relevant strategy should be