Existing methods for few-shot fine-grained image classification often suffer from feature selection bias, making it difficult to balance local and global information, which hinders the accurate localization of key discriminative regions. To address this issue, a multiscale joint distribution feature fusion metric model is proposed in this paper. First, a multiscale residual network is employed to extract image features, which are then processed by a multiscale joint distribution module. This module computes the Brownian distance covariance between the different scales, thereby integrating both local and global information to enhance the representation of important regions. Finally, an adaptive fusion module with attention mechanism based on global average pooling and Softmax weight normalization is used to dynamically adjust feature contributions and maximize the impact of key region features on the classification results. Experimental results indicate that classification accuracies of 87.22% and 90.65% are achieved on the 5-way 1-shot task of the CUB-200-2011 and Stanford Cars datasets, respectively, demonstrating significant performance in few-shot fine-grained image classification tasks.