Face anti-spoofing is a powerful guarantee for the practical security of facial recognition technology. However, the constant evolution of live attack methods poses significant challenges to existing detection methods. To address the increasing number of unknown scenarios and attack methods, a two-stream face anti-spoofing model based on visual attention and domain feature fusion is proposed. First, a visual attention-based feature extraction module is proposed to strengthen the model’s capacity to extract content features based on global information. Second, a novel style feature fusion module is designed to optimize the feature representation of the sample by fusing content features with low-level textural style features. Third, a feature mapping strategy based on the Siamese network is developed and the contrast loss function is modified to improve the model robustness and avoid easy gradient oscillation during training, respectively. Furthermore, domain adversarial training (DAT) is used to reduce the sensitivity of the model to differences between sample data domains and further improve its generalization. Extensive experimental results verify the generality and strong robustness of the proposed method, demonstrating that it outperforms existing models in cross-domain performance on mainstream datasets.