Key resource page is one of the most important search target pages for Web search users. Decision tree learning is one of the most widely-used and practical methods for inductive inference in machine learning. Because of the difficulty in uniform sampling of Web pages, there are not enough negative instances for training a key resource decision tree. To solve the problem, the original algorithm is partly modified to learn from global instead of individual instance information. With the same evaluation method as TREC (Text Retrieval Conference) 2003, large scale retrieval experiments based on improved decision tree algorithm achieves more than 40% improvement than the ones based on the original algorithm. It not only offers an effective way for selecting Web key resource pages, but also shows a possible way to improve decision tree learning performances.