网页内容提取在信息检索、文本分析以及网络资源数据处理等领域具有重要的工程与应用价值.针对网页中的大量无关内容及网页结构的异构性所造成的网页内容提取难题，提出一种基于文本对象模型(DOM)的自动化网页内容提取方法.首先，在节点过滤后，对网页的DOM模型进行压缩，便于后续分析处理；然后，提出基于文本-链接密度的内容提取方法来识别网页内容；最后，基于节点熵来识别并去除网页内容中的噪声链接.实验结果表明，相比于传统的网页内容提取方法，该方法的准确率和F1分数均有明显提升，而召回率仅有轻微下降.
Web content extraction has great engineering and application value in the fields of information retrieval, text analysis and network resource data processing. In view of the problem of web content extraction caused by useless information on web pages and the heterogeneity of web page structures, this paper proposes an automated web page content extraction method based on Document Object Model (DOM). Firstly, for DOMs generated from original web pages, we remove useless nodes from them and then compress the models, which facilitates subsequent processing. Then, we identify the web page content based on text and hyperlink density. Finally, we identify the noise hyperlinks based on node entropy and remove them from the content. The experimental results show that compared with the traditional methods of web page content extraction, the accuracy and F1 score of our method are obviously improved while there is only a slight decline on recall.
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