Knee OsteoArthritis (KOA) is a widespread musculoskeletal disorder that can severely impact the mobility of older individuals. Insufficient medical data presents a significant obstacle for effectively training models due to the high cost associated with data labeling. Currently, deep learning-based models extensively utilize data augmentation techniques to improve their generalization ability and alleviate overfitting. However, conventional data augmentation techniques are primarily based on the original data and fail to introduce substantial diversity to the dataset. In our study, we introduce a unique method that utilizes the Vision Transformer (ViT) model, enhanced by our newly developed Selective Shuffled Position Embedding (SSPE) and key-patch exchange techniques. These innovations generate varied input sequences, serving as a data augmentation strategy for improving KOA classification. More specifically, we fix and shuffle the position embeddings of key patches and non-key patches, respectively. Then, for the target image, we randomly select other candidate images from the training set to exchange their key patches and thus obtain different input sequences. Finally, a hybrid loss function is developed by incorporating multiple loss functions for different types of the sequences. According to the experimental results, the generated data are considered valid as they lead to a notable improvement in the model’s classification performance.