Recognition of weather phenomena based on images is essential for the analysis of weather conditions. To address the problems that traditional machine learning methods are difficult to accurately extract various weather features and poor in classifying weather phenomena and the accuracy of deep learning for weather phenomena recognition is not high, a weather recognition model based on image block and multi?headed attention mechanism is proposed. The model introduces Swin Transformer into the field of weather recognition for the first time, and adopts a multi?headed attention mechanism combining window multi?head self?attention layer and shifted?window multi?head self?attention layer, whose regionally relevant features extraction capability makes up for the shortcomings of traditional methods and can extract complex weather features from images. The model is trained using transfer learning, and the fully connected parameters of the fine?tuned model are input to the Softmax classifier to achieve recognition of multi?category weather images with 99.20% recognition accuracy, which is better than several mainstream methods in comparison, and it can be applied to ground weather recognition systems as a weather recognition module.