自动特征识别是智能制造的关键技术之一。传统的基于规则的识别算法可扩展性较差，而基于深度卷积网络的方法以离散模型为输入，准确度不高，且识别结果难以精确映射回原始计算机辅助设计（CAD）模型，造成应用不便。针对上述不足，该文提出了一种基于图神经网络的、能够直接处理边界表示（B-Rep）模型的加工特征识别方法。该方法首先从B-Rep结构中提取有效的属性和几何信息，形成特征描述符；接着根据CAD模型拓扑结构建立具有高级语义信息的邻接图；进而以邻接图为输入，构建高效的图神经网络模型，通过引入可微的广义消息聚合函数和残差连接机制，提升模型的信息聚合及多层级特征捕捉能力，同时采用消息归一化策略确保训练稳定性并加速收敛；训练完成后，网络能对B-Rep模型中的所有面进行分类标注，实现特征识别。将该方法在公共数据集MFCAD++上进行测试，取得了99.53%的准确率和99.15%的平均交并比，说明该方法优于现有的同类研究成果。采用更复杂的测试用例和工程应用中的典型真实CAD案例作进一步检验，结果均表明该方法具有更好的泛化能力以及更强的适应性。

Automatic feature recognition is one of the key technologies of intelligent manufacturing. Traditional rule-based recognition algorithms have poor scalability, and the methods based on deep convolutional networks are of low accuracy because they use discrete models as input and the recognition results are difficult to accurately map back to the original CAD model, causing inconvenience in application. In view of these shortcomings, a feature recognition method based on graph neural network, which can directly analyze B-Rep models, is proposed. The method extracts effective characteristic information and geometric information from the B-Rep structures to form a feature descriptor, and then establishes an adjacency graph with high-level semantic information based on the topological structure of the CAD model. By taking the adjacency graph as the input, an efficient graph neural network model is constructed. By introducing a differentiable generalized message aggregation function and a residual connection mechanism, the model possesses stronger information aggregation performance and multi-level feature capture capabilities. What is more, message normalization strategy is used to ensure the stability of the training process and to accelerate the convergence of the model. After the training, the network can directly classify and annotate all faces in the B-Rep model, thereby realizing feature recognition. Experimental results on the public dataset MFCAD++ demonstrate that the proposed method achieves an accuracy of 99.53% and an average intersection-over-union ratio of 99.15%, which outperforms other similar studies. Further evaluations using more complex testing cases and typical CAD cases from real engineering applications show that the proposed method is of better generalization ability and adaptability.