基于BP神经网络的山东旅游收入关键因子

Identification of Key Factors of Tourism Income in Shandong Province Based on BP Neural Network

在建立影响旅游收入关键因子的指标体系基础上,对影响旅游收入因子进行Pearson相关性删选,利用4层BP神经网络建立模型,对影响旅游收入的关键因子进行识别,结果表明:同一省份的不同城市之间的影响因子存在较大差异,青岛市等旅游收入高的城市主要受到"国内旅游人数"的因子影响;泰安市等旅游收入在中上等的城市主要受到"城镇居民消费支出"的因子影响;莱芜市等旅游收入低的城市主要受到代表历史文化的"博物馆数量"因子影响。交通因素与景区、度假区因素有密切联系,显示出交通的便捷性对提升景区、度假区的经营收入起促进作用。

This paper uses 4 layer BP neural network model,Pearson correlation analysis on influence factor of tourism income to identify the key facters.The study finds that there is a big difference between the cities of Shandong province tourism income factors.The cities with high tourism income are mainly influenced by the factors of"the number of domestic tourists".Tourism income in the middle and upper class cities is mainly influenced by the factors of"urban residents'consumption expenditure".The traffic factors are closely related to the factors of scenic spots and resort areas,which shows that the convenience of traffic plays a role in promoting the operating income of scenic spots and resort areas.