Research on Prediction Model of Hotels’ Development Scale Based on BP Artificial Neural Network Algorithm

Nan Zhao,Sang-Bing Tsai

doi:10.1155/2021/6595783

Abstract

Due to the lack of macro and systematic data, the target cost of high-star hotel project cannot meet the characteristics and needs of the hotel project itself. Therefore, the establishment of star hotel development scale prediction is urgent. In the scale development strategy, based on the previous studies, combined with the development characteristics of regional high-star hotels in a city, this paper constructs the index system of influencing factors of the development scale of high-star hotels and extracts the main influencing factors of hotel development scale by principal component analysis and partial relationship analysis, which are mainly urban development, economic development, tourism development, tourism development exhibition industry development, business development, and transportation development. The BP artificial neural network prediction method is used to establish a prediction model for the development scale of high-star hotels, by adopting the above key extraction factors as input of BP neural network. Through the input and output of the scale influence index data, the development scale of star hotels is accurately predicted. The simulation results verify the effectiveness and reliability of the star hotel development scale prediction strategy based on BP neural network, in terms of accuracy and model superiority.

Highlights

With the development of China’s economy and tourism, as the three pillar industries of tourism, the hotel industry is developing rapidly, and the star hotels are developing rapidly [1, 2]
Among the three-star hotels opened in the fourth quarter of 2012, the number of five-star hotels accounts for nearly 80% of the total number of opened hotels, occupying a dominant position, which reflects that high-star hotels have become the focus of the development of the hotel industry and the hot spot of investment [5, 6]
It can be seen from the figure that after Principal component analysis (PCA) algorithm, data dimension reduction is realized, redundant data is eliminated, and it is more convenient for data storage and data analysis

Summary

Introduction

With the development of China’s economy and tourism, as the three pillar industries of tourism, the hotel industry is developing rapidly, and the star hotels are developing rapidly [1, 2]. E factors related to the development scale of high-star hotels in the above literatures are total import and export, utilized foreign capital, number of foreign-funded enterprises, total retail sales of social consumer goods, total tourism income, total number of tourists, number of travel agencies, number of tourist attractions, number of international tourists, per capita public green space area, population (10000), urbanization level, GDP Per capita disposable income of urban residents, investment in fixed assets, per capita GDP, leading role of tertiary industry, passenger volume, transaction volume of exhibitions, and number of exhibitions [19]. E main factors that affect the development scale of star hotels are urban development, economic development, tourism development, exhibition industry development, business development, and transportation development.

Architecture Design of Star Hotel Data Mining and Data Dimension Reduction

Simulation Results and Performance Analysis

Conclusion