Analysis on Spatial Distribution Characteristics and Configuration of Tourist Hotels Based on Artificial Intelligence Technology

Abstract

As one of the three pillar industries of tourism, the hotel industry has developed rapidly in recent years, especially the construction and development of urban star hotels. The major star hotel brands have poured in one after another, and a large amount of funds have been invested in the star hotel market. The continuous construction of various types of star hotels has formed the geographical agglomeration phenomenon of star hotels in Chinese cities. This phenomenon has not only brought the advantages of regional economies of scale and brand building, but also brought the competition between star hotels. Under the guidance of market demand, tourism can be created through industrial integration and functional combination to develop the tourism industry, which makes the spatial distribution of tourism hotels closely related to transportation, consumer demand, urban environment, and even industrial policy. In recent years, artificial intelligence methods are more and more applied to tourism, mainly including rough set method, genetic algorithm, fuzzy time series, grey theory, artificial neural network, and support vector machine. Based on artificial intelligence technology, this paper studies the spatial distribution characteristics and configuration of tourism hotels. The distance of the peak value of tourism hotel spatial distribution characteristics is 4.8 km and 6.6 km, respectively, and the distance of the peak value of natural tourism resources is 0.98 km. Because the spatial distribution characteristics of cultural, special, and tourism hotels are restricted by their own quantity, resource endowment, and inherent attributes, the spatial scope of location layout is relatively small. The spatial distribution characteristics and configuration of tourist hotels can also timely monitor and prevent various emergencies, prevent tourism safety accidents, and improve the ability of tourism emergency management by using artificial intelligence and other technologies. With the continuous optimization of artificial intelligence technology and the deepening of cooperation between tourism disciplines and interdisciplinary, the research on tourism big data driven by research problems will be more mature.