Code smell is a great challenge in software refactoring, which indicates latent design or implementation flaws that may degrade the software maintainability and evolution. Over the past decades, a variety of refactoring approaches have been proposed, which can be broadly classified into metrics-based, rule-based, and machine learning-based approaches. Recent years, deep learning-based approaches have also attracted widespread attention. However, existing techniques exhibit various limitations. Metrics- and rule-based approaches rely heavily on manually defined heuristics and thresholds, whereas deep learning-based approaches are often constrained by dataset availability and model design. In this study, we proposed a graph-based deep learning approach for code smell refactoring. Specifically, we designed two types of input graphs (class-level and method-level) and employed both graph classification and node classification tasks to address the refactoring of three representative code smells: long method, large class, and feature envy. In our experiment, we propose a semi-automated dataset generation approach that could generate a large-scale dataset with minimal manual effort. We implemented the proposed approach with three classical GNN (graph neural network) architectures: GCN, GraphSAGE, and GAT, and evaluated its performance against both traditional and state-of-the-art deep learning approaches. The results demonstrate that proposed approach achieves superior refactoring performance.

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