In this study， a fine-grained remote sensing ship open-set recognition model is designed to address the limitations of traditional deep convolutional neural networks in fine-grained classification of ship images. First， a STN module based on attention mechanism is introduced before the feature extraction network to filter background information. In addition， a multi-scale parallel convolution structure is added after the STN module to enhance the feature extraction ability of the network for local regions of different scales. The extracted features are input into the base and meta-embedded branches， to increase inter-class variance and reduce intra-class variance， strengthening the model's learning of the tail class small samples concomitantly. Finally， the classification results of the two branches are fused； known and unknown classes are distinguished according to the set threshold； and known classes are subdivided. Four types of openness experiments were conducted on the FGSCR-42 datasets with balanced and unbalanced distributions. The results show that the average accuracies of the four types of openness in the balanced distribution dataset are 90.5%， 86.3%， 85.7%， and 85.1%； the corresponding average accuracies of the unbalanced distribution dataset are 90.0%， 85.1%， 84.3%， and 84.1%. Compared with the current mainstream ship recognition methods， the proposed method has higher recognition accuracy and better generalization ability.