Study on Tourists'Landscape Preferences of National Park Based on Machine Learning
In order to accurately explore the landscape preferences of national park visitors and to provide effective strategies for the related work of national park,the paper takes Yunnan Pudacuo National Park as a research case.After reviewing the existing literature,it determines to use social media data and machine learning to assess the landscape preferences of visitors to the park,in order to supplement the shortcomings of the previous research.In the specific research progress,Python 3.11 is used as a tool to collect and organize the related information of the park from various online platforms.The cascaded multimodal factorized bilinear pooling technique in machine learning is employed to meticulously process the collected data and to extract the specific elements from image files.Subsequently,in terms of the data process results,label detection and multiple feature detection methods are utilized to analyze the distribution of these elements on different online platforms,thus initiating a preliminary exploration on tourist preferences.The research findings reveal that users on Weibo,Tiktok,and Mafengwo App exhibit distinct preferences for the landscape elements of Pudacuo National Park,each displaying unique characteristics.Finally,based on the summarized analysis results,this paper provides relevant suggestions for the subsequent work of the park management unit from several perspectives to improve the attraction of natural environmental elements to the tourists.
social media datamachine learningtourist landscape preferences
国家科技期刊平台
NSTL
万方数据
维普
