美国来华旅游需求的影响因素——基于贝叶斯正则化BP神经网络下的实证  

A study on the influencing factors of American tourism demand for China:Demonstration based on Bayesian regularized BP neural network

作　　者：王瑜铬 汪永旗[1] 

机构地区：[1]浙江旅游职业学院旅行社管理系,浙江杭州311231

出　　处：《浙江师范大学学报（自然科学版）》2017年第4期459-464,共6页Journal of Zhejiang Normal University:Natural Sciences

基　　金：浙江省公益技术应用研究项目(2016C33125);浙江省高等教育教学改革项目(JG2015286)

摘　　要：为研究影响入境旅游需求各因素的显著性,基于1990年以来的数据资料,利用贝叶斯正则化BP神经网络建模对美国来华旅游需求的相关影响因素进行训练,并对各个影响因素的权值进行分析.结果发现:1)中美两国的双边贸易额是影响入境旅游需求的主要因素;2)汇率变动对入境旅游需求的影响也较为显著;3)本国的经济发展状况对入境旅游需求也有一定的影响;4)美国的失业率对入境旅游需求的影响显著性较低.It was aimed to study the impact of factors affecting the inbound tourism based on available data since 1990. The Bayesian regularization BP neural network model was used to train the relevant influencing factors of the demand from US to China tourism,and the weight of each influencing factor was analyzed. The results showed that： 1） The bilateral trade volume between China and the United States was the main factor affecting the demand of inbound tourism; 2） The impact of exchange rate changed on the demand for inbound tourism was also significant; 3） The economic development of the country had a certain impact on the demand for inbound tourism; 4） The impact of the US unemployment rate on the inbound tourism demand was not significant.

关 键 词：BP神经网络 贝叶斯正则化 入境旅游 旅游需求 

分 类 号：F592[经济管理—旅游管理]