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旅游热门景点预测是当前旅游管理研究领域中的热点,针对传统旅游热门景点预测模型无法准确描述旅游热门景点的变化特点缺陷,为了提高旅游热门景点预测精度,提出基于粒子群算法优化神经网络的旅游热门景点预测模型。首先分析当前国内外对旅游热门景点预测问题研究方法,得到旅游热门景点具有较大非线性变化特点,这也是导致当前旅游热门景点预测错误大原因,然后引入非线性建模能力强的RBF神经网络描述旅游热门景点的非线性变化特点,并对RBF神经网络参数进行优化,建立最优的旅游热门景点预测模型,最后与传统旅游热门景点预测模型进行了对比测试,结果表明,粒子群算法优化神经网络可以更好的跟踪旅游热门景点变化规律,旅游热门景点预测精度要明显优于传统旅游热门景点预测模型,而且旅游热门景点预测效率也更高,能够满足旅游热门景点在线预测要求。

Prediction of popular tourist attractions is a hot spot in the field of tourism management. Aiming at the defect that the traditional prediction model of popular tourist attractions can not accurately describe the changing characteristics of popular tourist attractions, a prediction model of popular tourist attractions based on neural network optimized by particle swarm optimization is proposed in order to improve the prediction accuracy of popular tourist attractions. Firstly, this paper analyzes the current domestic and foreign research methods on the prediction of popular tourist attractions, and obtains that popular tourist attractions have large non-linear change characteristics, which is also the major reason for the current prediction errors of popular tourist attractions. Then, an RBF neural network with strong non-linear modeling ability is introduced to describe the non-linear change characteristics of popular tourist attractions. At last, the RBF neural network parameters are optimized, and the optimal prediction model of tourist hot spots is established. Finally, the comparison test with the traditional prediction model of tourist hot spots is carried out. The results show that the neural network can better track the changing law of tourist hot spots, and the prediction accuracy of tourist hot spots is obviously better than the traditional prediction model. The prediction model of tourist attractions is more efficient and can meet the online prediction requirements of tourist attractions.