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Rural Tourism Image Optimization by Big Data Technology in Green Environment

Paper Topic: 
Ecology, environmental change and management

Pages :
50 - 56

Corresponing Author: 
Lanlan Li
Lanlan Li
Paper ID: 
Paper Status: 
Date Paper Accepted: 
Paper online: 
Visual abstract: 

In recent years, the need for the rapid development of rural tourism in China has resulted in a large consumption of ecological resources, which has seriously damaged the ecological balance of travel destinations. Rural tourism in many parts of China is increasingly lacking distinctive local characteristics. It is difficult to adapt to tourists’ requirements for a deeper experience of rural travel. Some measures have been proposed to address these issues. Firstly, the connotation of “green” is reinterpreted based on relevant theoretical knowledge starting from solving the problem of rural tourism community development in China. The questionnaire method is used to screen the indicator system. An empirical analysis is carried out for actual cases. The pictures uploaded by tourists to social media are the main data source. A relatively complete and effective method system for the image research of rural tourism destinations is constructed combined with the visual analysis technology based on deep learning. A dense CNN model producing 10 output parameter combined with various factors is formed to assist the promotion by comparing the differences between the advertising pictures of the tourist destination and the pictures taken by tourists. The results show obvious consistency in the construction scene of the rural travel perception environment. Each tourist destination has its own visual characteristics, as well as differences in various sensory modality types. This research method adopts a combination of subjective and objective and comprehensively examines the scene conspicuousness, distinction, artistic quality, design quality, and time of the pictures. The formed marketing picture research method is more scientific and feasible.

ecological resources, green evaluation; rural tourism image; photo big data; deep learning