Internet Tourism Resource Retrieval Using PageRank Search Ranking Algorithm

At present, there is a wide variety of tourism resources on the Internet. Tourism management departments must monitor these resources. At the same time, tourists must also retrieve personalized information that they are interested in. This requires a lot of time and energy. This essay studies and implements the tourism network resource monitoring system. The main work completed in the thesis proposes and constructs a topic collection algorithm and establishes a starting point, topic keywords, and a prediction mechanism. The algorithm includes three stages: the first climbing stage, the learning stage, and the continuous climbing stage. Open category directory search is used for similarity judgment and result evaluation. The experimental results show that with the continuous execution of the crawling process, the collection speed of related pages is getting faster and faster. We propose an algorithm for the extraction of wood based on the density of Internet tourism resources. The algorithm calculates the ratio of Internet tourism resource labels by row and uses a threshold extraction algorithm to distinguish area from private non-Internet tourism resource area. Experimental results show that the algorithm can successfully extract the main content of the article from a wide variety of web pages. This thesis takes the monitoring of tourism network resources as the research object and establishes a tourism network resource monitoring system, which can provide users with customizable, all-round, and real-time tourism network resource collection, extraction, and retrieval services so as to monitor tourism resources. The research results of this article can promote the construction of tourism informatization and can help users grasp the latest tourism information, thereby bringing great convenience to tourism. The system only downloads travel-related information through the use of topic collection technology, reducing the interference of irrelevant redundant web pages.