A multi-scale improved deep residual shrinkage network (MS-IDRSN) based rolling bearing life state identification method is proposed to address the issue of reduced domain adaptation performance caused by background noise interference in spatial rolling bearing data under different operating conditions, resulting in a decrease in accuracy of life state recognition. Firstly, an improved threshold function is introduced into the contraction layer of the deep residual contraction network to enhance the network's noise resistance and reduce the loss of life state information during the denoising process; Then, using convolutional kernels of different sizes to obtain deep fusion denoising features, the generalization ability of the features in noisy backgrounds is improved. Finally, by minimizing the maximum mean difference and adapting the feature distribution of the source domain and target domain features, the recognition of bearing life status under different operating conditions is achieved. The applicability and effectiveness of MS-IDRSN were validated on the PRONOSTIA dataset and self-test bearing dataset, and the results showed that the proposed method has good noise resistance and generalization performance. Under high noise conditions, compared with the comparison method, the accuracy of life state recognition improved by 7.6% to 46.5%.