An Empirical Study on Intelligent Rural Tourism Service by Neural Network Algorithm Models

• Published in: Complexity (New York, N.Y.) Vol. 2021; no. 1
• Main Authors: Chen, Jingzhi, Xue, Hongbo, Tsa, Sang-Bing
• Format: Journal Article
• Language: English
• Published: Hoboken Hindawi 2021; John Wiley & Sons, Inc; Wiley
• Subjects: Algorithms; Classification; Distribution (Probability theory); Distribution functions; International economic relations; Iterative methods; Lagrange multiplier; Neural networks; Neurons; Random variables; Tourism; Tourist attractions; Travel; Travel industry; China
• ISSN: 1076-2787; 1099-0526; 1099-0526
• DOI: 10.1155/2021/4156904

With the continuous development of social economy, tourism has become one of the many choices and is becoming more and more popular. However, it should be noted that how to provide high-quality and efficient tourism services is extremely important. This paper introduces the neural network algorithm and the optimal classification decision function, through unified combing, classification, and coding of scenic spots, to achieve the subclass classification of scenic spots, based on the optimal distribution function of random intelligent selection, and the formation of the corresponding scenic spots traversal clear tourism routes. The corresponding motivation iteration is obtained by using the corresponding travel route transmission, the best travel route is defined, the corresponding auxiliary decision support is provided, and the simulation experiment is carried out. The experimental results show that the neural network algorithm and the optimal classification decision function are effective and can support the intelligent decision assistance of rural tourism service.