###### Abstract
Due to its combination of self-attention processes, the Transformer can be used to serialize images, parallelize training, and capture global information. More and more scholars have explored using the Transformer for fine-grained image classification tasks. [PERSON] [15] proposed the Vision Transformer model, which improves classification accuracy by extracting global features from images through the introduction of self-attention modules. However, this model can only capture correlations between pixels within a single sample, limiting its feature-extraction capability and significantly increasing the number of model parameters [16]. [PERSON] et al. [17] were the first to propose the Transformer-based fine-grained image recognition framework, which uses a self-attention mechanism to capture the most discriminative regions in an image, processes the internal relationships between regions using image blocks, and uses a loss function to expand the distance between similar subclass feature representations. Because this method's input image blocks are fixed, it has weak adaptability, a high computational cost, and low accuracy on certain datasets. [PERSON] [18] proposed a multi-stage ViT framework for fine-grained image recognition. The framework utilizes multi-head self-attention mechanisms to capture distinctive image features from different local regions. Additionally, different attention-guided enhancements are employed to augment the model and learn diverse discriminative features, thereby improving the model's generalization capability. [PERSON] [19] researched how to incorporate the locality mechanism into the Vision Transformer, verified the significance of the locality mechanism, and successfully applied the proposed locality mechanism in four different Vision Transformers. [PERSON] [20] introduced an efficient multi-head self-attention mechanism (EMSA), which employs a simple depth-wise convolution technique to reduce memory usage. We have incorporated EMSA into our contribution and made appropriate enhancements within the improved Transformer Encoder. [PERSON] [21] proposed the concept of knowledge distillation to induce low-complexity Student models for training by introducing soft targets associated with the Teacher model as part of the total loss so that knowledge transfer can be achieved by training large models to obtain smaller models that are more suitable for inference. [PERSON] [22] presented the Deit model, which did not improve on the ViT model and instead used a knowledge-distillation approach to augment the predictive information of negative samples during model training while inheriting the Teacher model's inductive bias capability. [PERSON] [23] introduced a knowledge-distillation approach for training deep neural networks, dramatically improving the neural network's generalization ability and producing exceptional performance in image classification and facial expression recognition. In the research of fine-grained image classification tasks, both the CNN and the Transformer have demonstrated their effectiveness to varying degrees. The CNN excels at capturing local features and texture information, while the Transformer proves superior in capturing global information and long-distance dependencies. Through knowledge distillation, the Deit model [22] successfully combines the strengths of both the CNN and the Transformer, achieving impressive results in coarse-grained image classification tasks. In contrast, previous studies on the blast furnace tuyere [24, 25] were confined to CNN-based methods. Despite their high classification accuracy, these methods exhibited limited generalization capabilities. Motivated by these insights, this paper proposes a Blast Furnace Tuyere Image Classification Model (BDiT) based on knowledge distillation and a Vision Transformer. In this model, the CNN serves as the Teacher model, and an enhanced Transformer acts as the Student model, facilitating transfer learning through knowledge-distillation strategies to enhance the overall model performance.