With the exponential growth of both the amount and the diversity of the web information, web site mining is highly desirable for automatically discovering and classifying topic-specific web resources from the World Wide Web. Nevertheless, existing web site mining methods have not yet handled adequately how to make use of all the correlative contextual semantic clues and how to denoise the content of web sites effectually so as to obtain a better classification accuracy. This paper circumstantiates three issues to be solved for designing an effective and efficient web site mining algorithm, i.e., the sampling size, the analysis granularity, and the representation structure of web sites. On the basis, this paper proposes a novel multiscale tree representation model of web sites, and presents a multiscale web site mining approach that contains an HMT-based two-phase classification algorithm, a context-based interscale fusion algorithm, a two-stage text-based denoising procedure, and an entropy-base pruning strategy. The proposed model and algorithms may be used as a starting-point for further investigating some related issues of web sites, such as query optimization of multiple sites and web usage mining. Experiments also show that the approach achieves in average 16% improvement in classification accuracy and 34.5% reduction in processing time over the baseline system.