In this paper, we propose a convolutional neural network (CNN)-based method for software change-proneness prediction, aiming to utilize the powerful prediction ability of CNN to achieve better performance. Moreover, to alleviate the effect of the class imbalance problem, resampling methods are employed with the CNN. To validate the performance of the proposed CNN-based method, an empirical study was conducted. The experimental results show that the CNN-based method together with the resampling method performs better than baseline methods.