Abstract: To tackle the challenges, such as the low prediction accuracy of remaining electric life of AC contactor caused by single feature modeling, the insufficient consideration of correlation before and after opening, and disregarding the characteristics of long time series, we proposed a method for predicting the remaining electrical life of AC contactor using a data augmentation stacked denoised autoencoder-bidirectional gated recurrent unit (SDAE-BiGRU). First, the feature parameters were extracted from the AC contactor full-life test, and an optimal feature subset was selected using neighborhood component analysis (NCA) and Spearman rank correlation coefficient to characterize the degradation state of electrical life effectively. Then, the optimal feature subset was augmented to fully consider the correlation between the anterior and posterior states. The SDAE was employed to fuse and reconstruct the original feature information, in which the dimension was reduced and the computing speed was increased. Finally, the remaining electrical life of the AC contactor was treated as a long time series and predicted in time series prediction by BiGRU. The case analysis demonstrates that the model has better prediction accuracy than recurrent neural network (RNN), long short-term memory (LSTM), GRU, BiGRU and SDAE-BiGRU models, with an average effective accuracy of 96.68%. The feasibility of time series prediction model applied in the residual life prediction of electrical equipment is verified effectively.