This study proposes a novel log end face feature extraction and matching method based on Swin Transformer V2, aiming to address limitations in accuracy and speed faced by traditional deep learning models, like InceptionResNetV2 and Vision Transformer. Accurate log identification is crucial for forestry and wood supply chain management, especially given the growing reliance on timber imports to meet industrial demands in construction, furniture manufacturing, and paper production. Our dataset comprises images of coniferous timber, specifically Scots pine (Pinus sylvestris L.), reflecting its significance as an essential imported resource in China’s timber industry. By leveraging Swin Transformer V2 as the backbone, our method enhances feature extraction and achieves a significant accuracy improvement from 84.0% to 97.7% under random rotation angles while reducing the average matching time per log to 0.249 s. The model was evaluated under fixed and random rotation augmentations, and the results demonstrated Swin Transformer V2’s superior clustering ability, as confirmed by t-SNE visualization. Unlike InceptionResNetV2, the proposed model maintains high accuracy and efficiency even as the feature database size increases, making it suitable for large-scale applications. This approach provides a more accurate and efficient solution for log end-face recognition, supporting the development of high-throughput wood identification systems critical for forestry automation and the global timber trade.