We propose a framework for building a high-quality web page collection considering page group structure in a two-step process: rough filtering and accurate classification. In both processes, we apply the idea of local page group structure. The rough filtering comprehensively gathers all potential homepages from the web with as few noise pages as possible. It uses property-based keyword lists according to four page group models that are based on the page group structure. The accurate classification uses a textual feature set for the support vector machine, which is composed by independently concatenating the feature subsets on the surrounding pages grouped according to the page group structure. Using a combination of a recall-assured classifier and a precision-assured classifier, we build a three-way classifier to accurately select the pages that need manual assessment to assure the required quality. The effectiveness of proposed method is shown by the experimental results.