Research on the Management of Ecological Agricultural Characteristic Tourism Development Based on Artificial Intelligence Technology

Author:

Luo Zhide1

Affiliation:

1. 1 School of Economic and Trade Management, Yancheng Polytechnic College , Yancheng , Jiangsu , , China .

Abstract

Abstract Eco-agricultural characteristic tourism is one of the important contents of China’s tourism research, and is also one of the important initiatives to solve the “three rural problems” and realize comprehensive well-off. This paper proposes a particle algorithm-optimized RBF neural network model based on a neural network, which greatly overcomes the defects of its busy convergence speed and complex learning and calculation process. This paper uses this neural network to analyze and summarize the existing historical data on ecological and agricultural characteristics of tourism management, and presents the current problems of agricultural characteristics of tourism management to the tourist site managers in the form of data, to develop the ecological and agricultural characteristics of tourism management mechanism. The analysis of data focuses on passenger flow management, human resource structure management, and tourist satisfaction management in tourism management. The data calculation indicates that the staff at the scenic spot has an inadequate educational degree and lacks strong professionalism. In this regard, the eco-agricultural tourism attractions make corrective measures, the proportion of employees with a bachelor’s degree and above is pulled to 39%, and the average value of tourists’ experience of the attraction reaches 0.737. The neural network model proposed in this paper can effectively analyze and summarize the data of the characteristic tourism management, and provide data support for the improvement and development of the eco-agricultural characteristics of the management of tourism.

Publisher

Walter de Gruyter GmbH

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3