The increasement of blockchain applications has brought about many security issues, with smart contract vulnerabilities causing significant financial losses. The majority of current smart contract vulnerability detection methods predominantly rely on static analysis of the source code and predefined expert rules. However, these approaches exhibit certain limitations, characterized by their restricted scalability and lower detection accuracy. Therefore in this paper, we use graph neural networks to perform smart contract vulnerability detection at the bytecode level, aiming to address the aforementioned issues. In particular, we propose a novel detection model. In order to acquire a comprehensive understanding of the dependencies among individual functions within a smart contract, we first construct a Program Dependency Graph(PDG) of functions, extract function-level features using graph neural networks, then augment function-level features using a self-attentive mechanism to learn the dependencies between functions, and finally aggregate function-level features for detecting the vulnerabilities. Our model possesses the capability to identify the subtle nuances in the interactions and interdependencies among different functions, consequently enhancing the precision of vulnerability detection. Experimental results show the performance of the method compared to existing smart contract vulnerability detection methods across multiple evaluation metrics.