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However, most existing algorithms take the historical data and the emerging data as a whole and periodically repeat to analyze all of them, which results in heavy computation overhead. It is also challenging to accurately discover new knowledge in time, because the emerging data are usually small compared to the historical data. To address these challenges, we propose a novel knowledge discovery algorithm based on double evolving frequent pattern trees that can trace the dynamically evolving data by an incremental sliding window. One tree is used to record frequent patterns from the historical data, and the other one records incremental frequent items. The structures of the double frequent pattern trees and their relationships are updated periodically according to the emerging data and a sliding window. New frequent patterns are mined from the incremental data and new knowledge can be obtained from pattern changes. 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