Traditional optical pattern recognition methods encounter challenges in recognition accuracy and computational efficiency under the influence of atmospheric turbulence. However, deep learning methods, particularly convolutional neural networks and Swin Transformers, have gradually become effective tools for pattern recognition owing to their robust feature extraction and modeling capabilities. This study aims to improve the accuracy of multi-mode vortex optical pattern recognition under the influence of strong turbulence by developing a moving window multi-head self-attention mechanism and combining the global modeling advantage of Swin Transformers with the local feature extraction ability of a convolutional neural network. Experimental results demonstrate that the proposed model exhibits excellent performance under different transmission distances and turbulent conditions, particularly in a strong turbulent environment. At transmission distances of 1000, 1500, and 2000 m, the accuracy rates reach 99.87%, 98.50%, and 90.59%, respectively. Compared with the existing ResNet50 and EfficientNet-B0 methods, the proposed model exhibits considerable accuracy improvement under moderate and strong turbulence conditions, indicating its potential application in the field of vortex optical communication.