The diversity of base classifiers is an important factor to improve the accuracy and generalization ability of ensemble learning. The traditional post verification diversity measurement method is inefficient in big data environment. We therefore propose a process diversity measurement method based on information entropy. The method uses the gain of each attribute of the base classifier and its tree level to obtain the joint gain of the attribute set, and calculates the entropy distance between the classifiers to evaluate their diversity. It dynamically integrates the learning classifier with the entropy distance in accordance with the K-means method. Compared with traditional methods on watermelon dataset and other classification datasets, it is found that the proposed method has similar accuracy and higher computational efficiency.