A vulnerability or error in a smart contract will lead to serious consequences including loss of assets and leakage of user privacy. Established smart contract vulnerability detection tools define vulnerabilities through symbolic execution, fuzz testing, and other methods requiring extremely specialized security knowledge. Even so, with the development of vulnerability exploitation techniques, vulnerability detection tools customized by experts cannot cope with the deformation of existing vulnerabilities or unknown vulnerabilities. The vulnerability detection based on machine learning developed in recent years studies vulnerabilities from different dimensions and designs corresponding models to achieve a high detection rate. However, these methods usually only focus on some features of smart contracts, or the model itself does not have universality. Experimental results on the publicly large-scale dataset SmartBugs-Wild demonstrate that this paper's method not only outperforms existing methods in several metrics, but also is scalable, general, and requires less domain knowledge, providing a new idea for the development of smart contract vulnerability detection.