In the field of plant taxonomy, species often exhibit high levels of similarity in morphological features, color expression, and surface textures, while also containing rich and complex detail information. These characteristics pose significant challenges in the identification and classification process. Traditional machine learning cannot extract features comprehensively and accurately. This study leverages the Swin Transformer combined with image enhancement algorithms for plant image classification. On one hand, it benefits from enlarging the inter-class distance to improve classification accuracy; on the other hand, it addresses the issue of high computational complexity in large-scale plant image processing. By integrating the Swin Transformer with advanced image enhancement techniques in the task of plant image classification, a significant performance improvement has been achieved. Compared to using the Swin Transformer method alone, this integrated strategy has shown superior results, achieving an accuracy of 89.03% in plant classification tasks. This paper focuses on the plant image classification process based on the Swin Transformer with image enhancement algorithms.