In order to pay more attention to learning for probability density distribution of original samples in imbalanced data classification problem, an oversampling algorithm based on the gradient penalty generation adversarial network (OGPG) was proposed. Firstly, generation adversarial network (GAN) was adopted to effectively learn the probability density distribution of original data. Secondly, the gradient penalty was used to constrain the gradient two-norm of the input term of discriminator, which reduced the overfitting and gradient disappearance that appeared easily in GAN, so that the new samples were reasonably generated. In the experiment, the k-nearest neighbor and decision tree classifiers were adopted to compare the other oversampling algorithms, the evaluation indicators were significantly improved. The Wilcoxon signed-rank test was used to verify the statistical difference between this algorithm and the comparison algorithm. The results show that this algorithm has good effectiveness and generality.