<p>In the information age, news serves as a critical medium for information acquisition, whose importance is undeniable. However, the widespread dissemination of fake news poses a significant threat to society. Despite the efforts of many research teams to develop automated fake news detection technologies, these methods are often limited to specific domains, such as politics or healthcare. News coverage spans a vast range of fields, each with unique terminology, language style, and themes. In real-world scenarios, news articles are typically assigned a single domain label for classification, even though they often contain content across multiple domains. In this paper, we propose a novel Topic-guided Multi-domain Fake News Detection Framework (TG-MFEND) to address these challenges. TG-MFEND employs an Adaptive View Fusion Module to model news from multiple perspectives, including semantics, style, and themes. Additionally, we designed a Topic Domain Embedder to capture multi-domain features of news. Leveraging these multi-domain features, TG-MFEND adaptively aggregates features from different views to aid in determining news veracity. Experimental results on different datasets demonstrate that TG-MFEND achieves superior performance.</p>