Long Method is a code smell that frequently happens in software development, which refers to the complex method with multiple functions. Detecting and refactoring such problems has been a popular topic in software refactoring, and many detection approaches have been proposed. In past years, the approaches based on metrics or rules have been the leading way in long method detection. However, the approach based on deep learning has also attracted extensive attention in recent studies. In this paper, we propose a graph-based deep learning approach to detect Long Method. The key point of our approach is that we extended the PDG (Program Dependency Graph) into a Directed-Heterogeneous Graph as the input graph and used the GCN (Graph Convolutional Network) to build a graph neural network for Long Method detection. Moreover, to get substantial data samples for the deep learning task, we propose a novel semi-automatic approach to generate a large number of data samples. Finally, to prove the validity of our approach, we compared our approach with the existing approaches based on five groups of datasets manually reviewed. The evaluation result shows that our approach achieved a good performance in Long Method detection.