Domain adaptation has attracted attention by leveraging knowledge from well-labeled source data to facilitate unlabeled target learning tasks. Numerous research efforts have been devoted to extracting effective features by incorporating the pseudolabels of target data. However, the transferable knowledge reflected by intradomain structure, interdomain correlation and label supervision has scarcely been considered simultaneously. In this paper, we propose a novel structure preservation adversarial network with target reweighting (SPTR) for unsupervised domain adaptation, in which local structure consistencies and category-level semantic alignment are simultaneously considered in the adversarial learning framework. Based on the labeled and pseudolabeled samples, we attempt to align both global and category-level domain statistics from different domains and simultaneously enforce structural consistency from feature space to label space in the source and target domains. Furthermore, to suppress the influence of falsely labeled target samples, a novel and generalized sample reweighting strategy is developed to assign target samples with different levels of confidence, which fully explores the knowledge of the target distribution to benefit the semantic transfer process. The experimental results in three transfer learning scenarios demonstrate the superiority of our proposed method over other state-of-the-art domain adaptation algorithms. [ABSTRACT FROM AUTHOR]