Distinguishing genuine news from false information is crucial in today’s digital era. Most of the existing methods are based on either the traditional neural network sequence model or graph neural network model that has become more popularity in recent years. Among these two types of models, the latter solve the former’s problem of neglecting the correlation among news sentences. However, one layer of the graph neural network only considers the information of nodes directly connected to the current nodes and omits the important information carried by distant nodes. As such, this study proposes the Extendable-to-Global Heterogeneous Graph Attention network (namely EGHGAT) to manage heterogeneous graphs by cleverly extending local attention to global attention and addressing the drawback of local attention that can only collect information from directly connected nodes. The shortest distance matrix is computed among all nodes on the graph. Specifically, the shortest distance information is used to enable the current nodes to aggregate information from more distant nodes by considering the influence of different node types on the current nodes in the current network layer. This mechanism highlights the importance of directly or indirectly connected nodes and the effect of different node types on the current nodes, which can substantially enhance the performance of the model. Information from an external knowledge base is used to compare the contextual entity representation with the entity representation of the corresponding knowledge base to capture its consistency with news content. Experimental results from the benchmark dataset reveal that the proposed model significantly outperforms the state-of-the-art approach. Our code is publicly available at https://github.com/gyhhk/EGHGAT_FakeNewsDetection.