Conditional generative adversarial networks(CGAN) can learn its distribution characteristics from the data, and is introduced into the imbalanced data processing to oversample the minority class samples, which can generate new samples that conform to the original data distribution, so it has a better processing effect than traditional resampling methods. However, the learning of data distribution characteristics by a CGAN is easily limited by the sample size. When the sample size of the minority class is small, its distribution characteristics cannot be fully learned, and it is difficult to ensure the quality of the generated samples. To solve this problem, this paper proposes an unbalanced data balance processing method combined with the CGAN and the synthetic minority over-sampling technique edited nearest neighbor(SMOTEENN). Firstly, starting from the existing minority class samples, the SMOTEENN method is used to generate a certain scale of minority class samples, and then the CGAN model is trained on this basis to ensure that it can generate consistent the new samples with the distribution characteristics of the original minority class samples. Finally, the CGAN is used to regenerate new samples that conform to the original minority class sample distribution to construct a balanced dataset. The experimental results show that, compared with several classical imbalanced data processing methods and methods reported in recent literature, the proposed method has obvious advantages in several imbalanced data classification evaluation indicators.