<p>In the study of small datasets, obtaining training samples from source categories for learning is a challenge in fine-grained image classification. Based on the fact that fine-grained concepts can be learned with very few samples, only a small number of labeled samples (for example, three) are used for each category. However, due to the difficulty in distinguishing the subtle differences between fine-grained images, this paper proposes a method based on spatial frequency information feature fusion for small dataset fine-grained image classification (SDFGIC). This method not only considers the differences in features between the spatial and frequency domains of the images but also incorporates an image processing stage where the images are rotated multiple times. Since the convolutional kernel extracts features of a certain area in the image through translation and convolution operations, multiple rotations can obtain the feature representations of the images in different directions. Finally, learnable parameters are set to fuse the spatial and frequency domain features, and classification is performed through the fully connected layer. According to the mini-batch settings, the experimental results show that the proposed method performs better than other advanced algorithms in experiments on six small datasets.</p>