Upgrading and Optimization of Ski Tourism Resort Using Data Mining and Data Fusion

Abstract

After years of development, the ski industry has formed a prototype of a ski tourism industry cluster integrating skiing, skiing entertainment, skiing technology, and skiing culture. With the rapid development of global information, data mining and data fusion have attracted extensive attention as high-tech technologies for extensive data processing and useful information retrieval. The principles of these two data processing technologies are different, but they are functionally compatible with each other. This paper will use the methods of data mining and data fusion to research and analyze the upgrading and optimization of ski tourism destinations; eliminate unnecessary information; reduce rules; establish a fusion system according to the basic rules of rough sets; use industrial cluster theory, industrial structure and optimization theory, industrial economy, and sports tourism management theory to study the current situation and existing problems of ski tourism; and analyze the mechanism and theory of upgrading and optimization of ski tourism industry structure based on industrial clusters. The results show that the per capita ecological footprint of the ski resort has decreased by 0.06178 hm2 in the past five years, and the total ecological footprint of the whole region in 2020 is 535089.3 hm2. Compared with 2016, it increased by 0.97%, exceeding the ecological deficit carrying supply by 5 times. Compared with traditional algorithms, the performance of data mining and data fusion algorithms is improved by 48%. It is concluded that the development strategies of the ski tourism industry include the innovation of ice and snow sports products, the innovation of technology development paths, the optimization of the management mode and marketing mode of the ski tourism industry, and the innovation of industrial systems and systems.