A method for recognizing machining features based on graph neural networks was proposed in order to address the difficulties in identifying intersecting features and accurately determining machining feature surfaces in existing deep learning-based approaches. Features of nodes and adjacent edges were extracted through a compression activation module, and a dual-layer attention network at the node and adjacent edge levels was constructed in order to segment the machining features corresponding to each node. The surface features and edge features of the part model were fully used combined with the topological structure of the part model. The recognition problem of non-face merged intersecting features was effectively addressed by employing attention mechanisms for deep learning on the feature information. The proposed method was experimentally compared with three other feature recognition methods on a dataset of parts with multiple machining features. The optimal results were obtained in terms of accuracy, average class accuracy and intersection-over-union metrics. The recognition accuracy exceeded 95%.