In order to solve the problem that the accuracy of life prediction model of multi-component equipment decreases due to the difference in the distribution of degraded data under different working conditions, a domain feature fusion network (DFF-Net) which can adapt to different working conditions is proposed in this paper. Firstly, the degraded data of different working conditions were input into the feature extraction network to obtain the cross-working conditions characteristics. Then, the domain feature fusion network (DFF-Net) was used to adjust the cross-working conditions characteristics. Finally, the adjusted data was input into the life prediction model to output the life prediction results of the equipment under different working conditions. Tests on public data sets show that the MAE and RMSE of the predicted results of the proposed model on the test set decrease by 6. 5% and 7. 4%, respectively, compared with the lifetime prediction model without adding the domain feature fusion network, which indicates that the proposed model can effectively improve the accuracy of cross-working condition equipment life prediction.