There is a severe class imbalance problem in defect datasets, with nondefective data dominating the distribution, making it easy to generate inaccurate software defect prediction models. Ensemble learning has been proven to be one of the best methods to solve class imbalance problem. Traditional ensemble prediction models usually ensemble the results of several base classifiers simply, and most of them only ensemble once, rarely consider the diversity of ensemble or the combination of ensemble learning and neural network. In order to explore whether the secondary ensemble of classifiers based on a diversity ensemble combined with neural network can improve the performance of defect prediction model, in this article, we propose a novel dual ensemble software defect prediction (DE-SDP) approach based on a diversity ensemble combined with neural network. In the first ensemble, we use cross-validation to build different subclassifiers, then, these subclassifiers are used to establish base ensemble classifiers with weighted average method. Through seven classification algorithms, seven base ensemble classifiers can be established. In the second ensemble, a neural network model and stacking are used to ensemble the base ensemble classifiers again. We have evaluated DE-SDP against other ensemble defect prediction methods on eight datasets of NASA MDP. The results show that our approach is superior to other ensemble approaches and effectively improves the performance of defect prediction model.