Domain adaptation technologies have been extensively explored and successfully applied to machine fault diagnosis, aiming to address problems that target data are unlabeled and have a certain distribution bias with source data. Nonetheless, existing fault diagnosis methods mainly explore feature-level alignment strategies to reduce domain discrepancies, which not only fails to directly ascertain the relationship between the target output and domain deviation, but also cannot guarantee accurate diagnosis results (i.e., learning class-discriminative features) when only relying on feature adaptation. In light of these issues, a more intuitive and effective domain adaptation method is developed for intelligent diagnosis of machinery in this article, in which the minimum class confusion and maximum nuclear norm-based target prediction constraints are simultaneously designed to promote learning reliable domain-invariant and discriminative features for accurate fault diagnosis. We conduct extensive experiments based on two different mechanical systems to evaluate the proposed method. Comprehensive results and discussions demonstrate the promising performance of our approach.