Software vulnerabilities pose a great threat to network and information security， and the root of vulnerabilities lies in software source code. Existing traditional static detection tools and deep learning based detection methods do not fully represent code features， and simply use word embedding method to transform code representation， so that their detection results have low accuracy and high false positive rate or high false negative rate. Therefore， a source code vulnerability detection method based on hybrid code representation was proposed to solve the problem of incomplete code representation and improve detection performance. Firstly， source code was compiled into Intermediate Representation （IR）， and the program dependency graph was extracted. Then， structural features were obtained through program slicing based on data flow and control flow analysis. At the same time， unstructural features were obtained by embedding node statements using doc2vec. Next， Graph Neural Network （GNN） was used to learn the hybrid features. Finally， the trained GNN was used for prediction and classification. In order to verify the effectiveness of the proposed method， experimental evaluation was performed on Software Assurance Reference Dataset （SARD） and real-world datasets， and the F1 score of detection results reached 95.3% and 89.6% respectively. Experimental results show that the proposed method has good vulnerability detection ability.
