Vehicle re-identification aims to identify vehicles from different cameras and has drawn much attention in the multimedia community. In recent years, significant achievements in vehicle re-identification have been made due to the development of neural networks and deep feature representations. However, existing deep models are regarded as “black box” methods considering the lack of explaining where they focus. With Class Activation Mapping (CAM), we can localize the discriminative image regions by utilizing convolutional feature maps. After answering the “where” question, the question of “how” to generate reasonable features appears to be substantial. In this paper, we propose the novel Spatially-Regularized Features (SRF) that can be extracted from discriminative regions in an explainable way. Specifically, we first provide an evaluation mechanism called Peak-to-Sidelobe Ratio (PSR) to measure the distribution of the convolutional feature maps. PSR outputs the strength of a matrix peak and can be used to indicate the attention intensity of a specific region. Moreover, we propose a spatially regularized loss to make the deep models focus on more reasonable and discriminative image regions. Note that no additional manual annotation data is involved in the training process, making the SRF an efficient and effective approach. Extensive subjective and objective experiments show that the proposed method significantly outperforms the state-of-the-art methods on three large-scale vehicle re-identification datasets.