%0 Journal Article
%T 基于主题分类的旅游路线推荐规划模型——以北京市为例<br>Tourism Route Recommendation and Planning Model Based on Topic Classification—Taking Beijing as an Example
%A 韩天祎
%A 白千雪
%A 李霈雯
%J Computer Science and Application
%P 2126-2136
%@ 2161-881X
%D 2021
%I Hans Publishing
%R 10.12677/CSA.2021.118218
%X <div style=\"text-align:justify;\">
	<span style=\"font-size:14px;\">随着经济的发展，旅游逐渐成为人们生活的刚需，但计划行程、旅途的疲惫常常牵绊住人们外出的步伐。因此，本文基于北京市景点的文本评论运用LDA模型、K均值聚类进行主题提取、运用TF-IDF值进行评价打分为用户推荐最适宜的景点，节省了用户阅读攻略、规划行程的时间。不同于以往的数据分析，文本评论可以更直接反映用户的想法、更接近实际。除此之外，对于被选出来的景点，通过转化为旅行商问题，运用运筹学的蚁群算法为用户合理规划路线，减少步行时间以及交通时间。<br />
With the development of the economy, travel has gradually become the need of people’s life, but the fatigue of planning trips and the exhaustion of the journey have hampered the pace of people going out. Therefore, based on the text review of scenic spots in Beijing, this paper uses LDA model, K-means clustering to extract topics, and TF-IDF value to evaluate the most suitable scenic spots, recommend for users, in order to save the user’s time of reading strategy and planning the trip time. Unlike previous data analysis, text comments can reflect users’ ideas more directly and be closer to reality. In addition, for the selected scenic spots, by transforming into a travelling salesman problem, the ant colony algorithm of operations research is used to plan the route reasonably for users to reduce walking time and traffic time.</span> 
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%K 文本挖掘，LDA主题模型，TF-IDF，K均值聚类，蚁群算法<br>Text Mining
%K LDA Theme Model
%K TF-IDF
%K K-Means Clustering
%K Ant Colony Algorithm
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=44800