A prediction model based on Wasserstein conditional generative adversarial network-gradient penalty ( WCGAN-GP) and convolution long and short-term memory network (ConvLSTM) is proposed to address the problem of unbalanced data caused by the difficulty of collecting fault data during the operating cycle of an aero-engine. First, a WCGAN-GP model is used to learn the deep distribution characteristics of the pre-processed time-series data; then, a generator is used to generate fault samples and mix them with real samples as a training set to input into the prediction model based on the ConvLSTM network for training. Through testing with CMAPSS data set, the results show that compared with the single real sample training prediction model, the performance indexes RMSE and score of the model using mixed data are reduced by 12. 65% and 48. 95% on average.