基于事件驱动的车型参数主题爬虫

Topic Crawler of Car Parameters Based on Event Driven

网络上的大量数据都隐藏在深层网络中,普通的主题爬虫只能抓取表层数据,而对深层数据的抓取则力不从心。通过建立模拟事件驱动模型,采用基于链接的BM改进过滤算法和基于向量空间模型的内容过滤算法,结合汽车车型参数主题,利用Htmlparser解析动态生成的页面,对其进行结构化处理后将数据存储于索引数据库中,由此实现车型信息的自动抽取和解析。实验结果表明,该系统模型针对同领域数据具有良好的事件触发适应性和高过滤准确率。

Large amounts of data on the Internet were hidden in the deep Web. Traditional topic crawler could only get surface data, and crawl the deep data was powerless. This paper established simulation model of Event-Driven, Used improved filtering algorithm based on BM by links, and content filtering algorithm based on VSM（vector space model）, with model parameters of car, used Htmlparser to analysis dynamically generated pages, processed their structures to store in database, thus achieve extraction and parsing of car＇s information. The results of experimental show that the model for the same field datas has a good event trigger adaptability and high filtering accuracy.