To address the problem that low accuracy of rolling bearing remain useful life ( RUL) prediction caused by the limited information of single source domain and the insufficient granularity of domain, a new method of RUL for rolling bearing based on multisource subdomain adaption network is proposed. Firstly, fast fourier transform is applied to the collected raw vibration signals to obtain the frequency-domain signals and it takes the frequency-domain signals as the input of the model. Secondly, to reduce the distribution difference between multiple source domains and target domains, all domains are mapped to a common feature space by one-dimensional convolution, and the local maximum mean discrepancy is used to align the degradation stage of each source domain and target domain in an independent feature space. Finally, the RUL of rolling bearing is obtained by comprehensive output of the module in different domains. The results on PHM2012 data set show that the prediction accuracy of proposed method is higher than the comparison method, and can effectively predict the RUL of rolling bearing.