The dissemination of false news in various domains has a serious impact on society. The problem of domain shift and cross-domain correlation of news between different domains also poses a great challenge to the prediction ability of the model. To address the above problems, this study proposes a multi-domain fake news detection method based on cross-feature perception fusion. This method can capture multiple feature differences in news between different domains, mine the correlations between news, and control the feature fusion strategy of the model in different domains from multiple dimensions. In addition, this study proposes a joint training framework that is adopted to train the proposed model. The model achieves a predictive F1 score of 92.84% and 85.49% on the English and Chinese datasets, respectively. Compared to the state-of-the-art model, the prediction results of the proposed model are improved by 1.16% and 1.07%, respectively.