The existing deep transfer learning-based diagnosis methods usually require that the same fault class space is shared by training and test data, which fail to effectively identify new faults. Thus, a multi-domain emerging fault identification method based on selective weighted adaptive network is proposed. Firstly, a one-dimensional convolutional neural network is adopted to extract depth discriminative features across domains. Then, a domain discriminator and multi-classifier structures are integrated to construct weight functions of source and target domains to adaptively measure the similarity across different categories. The adversarial learning strategy is utilized to effectively reduce the distribution differences of shared classes across domains. Finally, the Gaussian distribution-based fitting method is adopted to automatically discriminate weight thresholds to realize effective fault diagnosis of known faults and emerging faults in the target domain. Experiments are conducted on a gearbox transmission test rig, where the transfer diagnosis tasks under variable operation conditions are designed. The proposed method obtains 0. 8 E-score in various tasks. The effectiveness and the superiority of the proposed method are fully validated in comparison with other existing methods.