Smart contracts are self-executing programs that are executed on blockchain platforms, and they have been widely used in recent years. However, malicious exploitation of the characteristics of smart contracts has become a pressing problem in blockchain security. Most of the existing methods have the drawback of detecting only a single type of vulnerability. To solve this problem, this study proposes a model for detecting multiple vulnerabilities in smart contracts. We preprocessed the data and transformed the Opcodes of smart contracts' source code into a control flow graph. We then extracted node features that are suitable to be the input of a graph neural network using Sent2Vec and performed graph classification. The proposed model was evaluated using real smart contracts, and the experimental results demonstrated that the proposed model can simultaneously detect multiple vulnerabilities with high performance.