Deep learning has made great progress in many fields, such as computer vision and natural language processing. But the performance of traditional deep learning models will be seriously degraded when facing the domain shift, which means that the distribution of test data and training data is significantly different. A large number of Domain Generalization (DG) methods have been proposed to enhance the generalizability of models. However, traditional DG methods are based on the assumption that the category space of training data and test data is consistent, which is always untenable in practice. Therefore, this paper further studies the open-set domain generalization problem when the category spaces of training data and test data are inconsistent. This paper proposes an open-set domain generalization framework based on the Siamese network, which generates images in the unknown categories through patch-shuffling, and treats generated images as negative samples to negatively supervise models. Thus models are forced to learn the critical feature representations, the over-fitting of models reduces, and then the performance of models on open-set domain generalization tasks is enhanced. The experimental results show that the proposed framework achieves state-of-the-art on the two open-set domain generalization benchmarks.