Traditional feature recognition methods often struggle with the variability of features and the interference arising from intersecting features,an approach utilizing graph convolutional neural networks(GCN)was proposed.To levarage the attribute adjacency graph associated with the machining features,an initial node embedding vector matrix as a foundation for model training was designed.With thorough training on diverse datasets of machining features and experimental optimization of the model's parameters,the GCN model demonstrated proficiency in classifying machining features,achieving an overall recognition accuracy of approximately 99%.Comparative analyses have shown the method's superiority over classic graph matching techniques,highlighting its wide applicability and robustness in feature recognition tasks.
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