关键词: 深度学习；特征融合；区块链；智能合约；漏洞检测

Abstract: With the rapid development of blockchain technology, smart contract vulnerability detection has become a critical task for ensuring the security of blockchain systems. Existing advanced methods typically transform bytecode into graph structures and apply graph neural networks for feature learning, but they insufficiently exploit the semantic and structural characteristics of source code. To address this issue, this paper proposes ACF-VDF, a vulnerability detection framework that fuses Abstract Syntax Tree (AST) semantic features with Control Flow Graph (CFG) structural features. Specifically, the method constructs ASTs from smart contract source code to obtain syntax feature vectors that capture hierarchical and semantic information; meanwhile, contract bytecode is transformed into CFGs, and through global pooling and node feature extraction, structural feature vectors that reflect execution paths and structural dependencies are generated. The two feature vectors are jointly modeled and fed into a Transformer-based deep neural network classifier to detect vulnerabilities in smart contracts. Experimental results demonstrate that the proposed method improves the F1 score by 2.10%–34.28% compared with seven state-of-the-art detection methods, significantly boosting both accuracy and generalization performance in vulnerability detection.

Key words: deep learning; feature fusion; blockchain; smart contract; vulnerability detection