To address the difficulties of capturing the potential distinguishable features and subtle appearance differences in fine-grained image recognition tasks, dual-attention random selection global context fine-grained recognition network is proposed. Firstly, the ConvNeXt is taken as the backbone network, a dual-attention random selection module is proposed to perform channel random selection and spatial random selection on the features extracted at different stages, so that the network could focus on other potential subtle distinguishable features. Then, by using the global context attention module, the semantic information of top-level is applied to the middle-level to enhance the ability of the middle-level to locate potential subtle distinguishable features. Finally, the multi-branch loss is proposed, and classification loss is imposed on middle-level, top-level and concat-level characteristics. Combining the features extracted from different branches, the network is induced to obtain diverse distinguishable features. The network achieves the accuracies of 95.2%, 92.1%, 94.0% and 97.0% respectively on the three open datasets,Stanford-cars,CUB-200-2011,FGVC-Aircraft and dataset VMRURS in real scenes. The presented method in this paper greatly upgrades the comparison performance.