To solve the problem of how to measure diversity among weak classifiers created by AdaBoost as well as the over-adaptation problem of AdaBoost, an improved AdaBoost method based on double-fault measure was proposed, which was based on the analysis and study of the relationship between four diversity measures and the classification accuracy of AdaBoost. Firstly, Q statistics, correlation coefficient, disagreement measure and double-fault measure were selected for experiment on the data sets from the UCI (University of CaliforniaIrvine Irvine) machine learning repository. Then, the relationship between diversity and ensemble classifier's accuracy was evaluated with Pearson correlation coefficient. The results show that each measure tends to a stable value in the later stage of iteration; especially double-fault measure changes similarly on different data sets, increasing in the early stage and tending to be stable in the later stage of iteration. Finally, a selection strategy of weak classifier based on double-fault measure was put forward. The experimental results show that compared with the other commonly used ensemble methods, the test error of the improved AdaBoost algorithm is reduced by 1.5 percentage points in average, and 4.8 percentage points maximally. Therefore, the proposed algorithm can improve classification performance.