Vehicle re-identification involves matching images of vehicles across varying camera views. The diversity of camera locations along different roadways leads to significant intra-class variation and only minimal inter-class similarity in the collected vehicle images, which increases the complexity of re-identification tasks. To tackle these challenges, this study proposes AG-GCN (Attention-Guided Graph Convolutional Network), a novel framework integrating several pivotal components. Initially, AG-GCN embeds a lightweight attention module within the ResNet-50 structure to learn feature weights automatically, thereby improving the representation of vehicle features globally by highlighting salient features and suppressing extraneous ones. Moreover, AG-GCN adopts a graph-based structure to encapsulate deep local features. A graph convolutional network then amalgamates these features to understand the relationships among vehicle-related characteristics. Subsequently, we amalgamate feature maps from both the attention and graph-based branches for a more comprehensive representation of vehicle features. The framework then gauges feature similarities and ranks them, thus enhancing the accuracy of vehicle re-identification. Comprehensive qualitative and quantitative analyses on two publicly available datasets verify the efficacy of AG-GCN in addressing intra-class and inter-class variability issues.