随着互联网的网页数量呈现爆炸式增长, 传统的通用搜索引擎越来越遭人诟病, 查询不准、 深度不够等问题, 使用户倍感烦恼. 因此, 针对特定行业的垂直搜索引擎逐渐兴起, 与之相关的研究也日益受到重视. 网页分类是垂直搜索引擎的基础和难点, 分类器的好坏直接决定了一个垂直搜索引擎系统的性能. 基于朴素贝叶斯的垂直搜索引擎分类器通过CHI方法进行特征提取, 利用朴素贝叶斯模型对从互联网爬取的网页按内容类别进行分类. 实验结果表明, 该分类器对网页分类有着良好的表现, 为构建大型专业的垂直搜索引擎系统奠定了一定的理论基础.%Along with the explosive growth of Internet pages,traditional universal search engines are more and more complained for problems such as inaccurate search and insufficient depth.Therefore,vertical search engine for special industries gradually emerges, and the associated researches attract more and more attention. Internet page classification is the basis and difficult point of vertical search engine.The quality of the classifier directly determines the performance of a vertical search engine system. The vertical search engine classifier based on naive Bayes extracts the features through CHI method,and then by using the naive Bayes model,it classifies the pages crawled from the Internet according to the contents.The experimental result shows that such classifier has good performance in classifyingInternet pages,which provides certain theoretical foundation for the construction of large-scale vertical search engine system.
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