Due to the small inter-class differences and large intra-class differences of fine-grained images, the key to fine-grained image classification tasks is to find subtle differences between categories. Recently, Vision Transformer-based networks mostly focus on mining the most prominent discriminative region features in images. There are two problems with this. Firstly, the network ignores mining classification clues from other discriminative regions, which can easily confuse similar categories. secondly, the structural relationships of images are ignored, resulting in inaccurate extraction of category features. To solve the above problems, this study proposes two modules: dynamic adaptive modulation and structural relationship learning. The dynamic adaptive modulation module forces the network to search for multiple discriminative regions, and then the structural relationship learning module is used to construct structural relationships between discriminative regions. Finally, the graph convolutional network is used to fuse semantic and structural information to obtain predicted classification results. The proposed method achieves testing accuracy of 92.9% and 93.0% on the CUB-200-2011 dataset and NA-Birds dataset, respectively, which is superior to existing state-of-the-art networks.