Ultra-fine-grained visual classification (ultra-FGVC) targets at classifying sub-grained categories of fine-grained objects. This inevitably requires discriminative representation learning within a limited training set. Exploring intrinsic features from the object itself, e.g., predicting the rotation of a given image, has demonstrated great progress towards learning discriminative representation. Yet none of these works consider explicit supervision for learning mutual information at instance level. To this end, this paper introduces CLE-ViT, a novel contrastive learning encoded transformer, to address the fundamental problem in ultra-FGVC. The core design is a self-supervised module that performs self-shuffling and masking and then distinguishes these altered images from other images. This drives the model to learn an optimized feature space that has a large inter-class distance while remaining tolerant to intra-class variations. By incorporating this self-supervised module, the network acquires more knowledge from the intrinsic structure of the input data, which improves the generalization ability without requiring extra manual annotations. CLE-ViT demonstrates strong performance on 7 publicly available datasets, demonstrating its effectiveness in the ultra-FGVC task. The code is available at https://github.com/Markin-Wang/CLEViT.