Abstract
This article leverages the Pandas library in Python to process and analyze extensive datasets, focusing on national tourist attractions. It reveals that 2563 attractions have achieved the highest rating of 5.0. Cities such as Sansha, Wujiaqu, Yuxi, Yiyang, Tianmen, Alar, Weifang, Yantai, Daxing'anling, and Xingtai top the list for the most fully rated scenic spots. A comprehensive evaluation system using the Analytic Hierarchy Process (AHP) is constructed. NLP technology builds a keyword library to match scenic area descriptions, evaluating environment, culture, transportation, climate, and cuisine. City line levels and intercity distances are integrated into an urban evaluation model. The study selects the top 50 cities attracting foreign tourists. A multi-objective 0-1 planning model for tourism planning is formulated to maximize tourist cities and scenic spot ratings. The greedy algorithm generates an initial solution, refined by the simulated annealing algorithm to seek the global optimum.
Published Version
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