A residual life prediction method for different types of rolling bearings is proposed based on the subspace domain adversarial discriminant network ( SDADN) to address the issue of inconsistent distribution and characteristic scales of bearing degradation data caused by differences in structural dimensions, operating conditions, and other factors, leading to a decrease in life prediction accuracy. Firstly, the feature extractor can adaptively obtain deep degradation features for different types of rolling bearings by using an efficient channel attention mechanism to enhance the weight of important features in each channel and is used to train the label predictor. Then, in the asymmetric feature mapping framework, the domain discriminator and feature extractor are adversarially trained to minimize the orthogonal basis distance between the source and target domains in the representation subspace. By utilizing the property that the orthogonal basis in the representation subspace is not affected by feature scaling, the regression performance degradation caused by excessive feature scale changes is reduced, and domain adaptation among different types of rolling bearings is achieved. Finally, the trained feature extractor is used to extract the degradation features of the bearing, and the remaining lifespan is obtained by inputting them into the label predictor. The proposed method was validated on PRONOSTIA, XJTU-SY, and self-test datasets, and the experimental results showed that it can fully learn the distribution information of source domain features, effectively overcome the feature scale differences under different models, and improve the performance by 20% to 40% compared to other domain adaptive methods.