Traditional methods for identifying fossil organisms often rely on a paleontologist's knowledge and experience, while existing artificial intelligence recognition methods require large amounts of fossil training samples to achieve high accuracy. To address this issue, this paper attempts to use a combination of residual network and attention module and apply it to the identification of fossils in small-sample images.Methods:First, a residual network is used as the model's feature extraction module, and CBAM convolutional attention modules are embedded in the residual blocks of the residual network to improve the model's focus on fossil texture features, extract more comprehensive deep-level fossil image features, then use a prototype network in few-shot metric learning to calculate the extracted features, and finally train the best fossil discrimination model through multiple iterations. Results:This paper compares this method with five classical few-shot learning methods, and experimental results show that this method has the highest recognition accuracy. In the case of 1 and 5 samples, the accuracy is 86.32% and 94.21%, respectively, showing significant advantages in recognizing rare fossil samples.Conclusions: The proposed method in this paper adopts the prototype network, which is a common framework used in few-shot learning, as the backbone. The CBAM convolutional attention module is embedded into the residual block of the ResNet12 residual network to enhance the feature extraction ability of the network. This approach achieves high recognition accuracy with only a small amount of training data for fossil images, which solves the problem of traditional convolutional neural networks requiring a large number of fossil image samples for high accuracy despite the limited availability of such data. However, the current work in this paper only involves training few-shot models on seven species-level categories of fossil images collected, and the fossil dataset still needs further expansion to include more diverse categories. The future research direction of this paper is how to achieve high recognition accuracy using small-sample learning models in situations where multiple categories with significant intra-class differences exist.