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FOREST RESOURCES WANAGEMENT ›› 2021›› Issue (2): 164-170.doi: 10.13466/j.cnki.lyzygl.2021.02.022

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Research on Tourism Image Perception of Yandang Mountain

LIN Xinying(), LAI Pengcheng, CHEN Jianling, WU Yuanjing, HUANG Qitang()   

  1. College of Landscape Architecture,Fujian Agriculture and Forestry University,Fuzhou 350002,China
  • Received:2021-02-01 Revised:2021-04-02 Online:2021-04-28 Published:2021-06-03
  • Contact: HUANG Qitang E-mail:605585109@qq.com;fjhqt@126.com

Abstract:

Scenic spots are China's representative characteristics in ecological system protection and have gradually become one of the important tourist destinations in recent years.This paper selects the Yandang Mountain scenic spot as the representative of Wenzhou nature reserve,tries to study the tourist reviews of seven major tourism websites,including Xiecheng,Qunaer and Meituan to study their tourism image perception based on content analysis and IPA analysis methods.The results show that:(1)The Erling and Yilong scenic spots in the Yandang Mountain Scenic Area are the most frequently mentioned words by tourists with a "core-sub core-peripheral" semantic network layout.(2)Positive emotions are mainly found in the tourists,and their neutral and negative emotions are under control.(3)Based on the perception of tourists,the tourism image of Yandang Mountain Scenic Area can be summarized into six major items:tourism resources,infrastructure,entertainment projects,service management,cultural experience,and emotional evaluation which can be subdivided into 16 elements for research.(4)The specific comments of the 16 constituent elements were sequentially coded and analyzed.The IPA model analysis was constructed to find that the performance scores of each element perceived by tourists are higher,but the importance scores are more scattered.Among them,natural landscape,public transportation,scenic spot management,and atmosphere evaluation are the advantage elements,and there are no weak elements.

Key words: tourism image perception, Yandang Mountain, web text analysis, IPA analysis, natural reserves, scenic areas

CLC Number: