Domain generalization person re-identification models can be trained in the source dataset and tested in the target dataset, which has wide practical application significance. The existing domain generalization models tend to focus on solving the problem of illumination and color change, and ignoring the effective utilization of detailed information, which leads to the low recognition rate. In this paper, a domain generalized person re-identification model is proposed, which is based on the attention mechanism. Firstly, the model extracts multi-scale features with different visual fields through the design of the bottleneck layer superimposed on the convolutional layer, and a feature fusion attention module is used to perform dynamic fusion of multi-scale features and assign weights. Then the semantic information of refinement features is mined by the multilevel attention module. Finally, the feature containing rich semantic information is input to the discriminator for person re-identification. In addition, a style regularization module is designed to reduce the influence of image light and shade changes on model generalization ability. Abundant comparison and ablation experiments conducted on market-1501 and DukeMTMC-reID datasets demonstrate the effectiveness of the proposed method.