Compared with other fine-grained image classifications, that of wild snakes is more difficult and complicated, as it is difficult to judge and classify snakes by their local characteristics due to their different postures, rapid posture changes, and usual status of motion or coiling. In response, this study applies the self-attention mechanism to fine-grained wild snake image classification to solve the problem that the convolutional neural network focuses too much on the local parts to ignore the global information due to the increasing number of layers. Transfer learning is implemented through Swin Transformer (Swin-T) to obtain a fine-grained feature extraction model. To further study the performance of the self-attention mechanism in meta-learning, this study improves the feature extraction model, builds a Siamese network, and construct a meta-learner to learn and classify a small number of samples. Compared with other methods, the proposed method reduces the time and space consumption caused by feature extraction, improves the accuracy and efficiency of meta-learning classification, and increases the learning autonomy of meta-learning.