Abstract: The development and progress of traditional source code defect analysis techniques rely mainly on analysts' understanding of safety issues and long-term experience. To improve the quality of source code defect detection and report, a source code defect detection method was proposed based on deep learning algorithm. Firstly, introducing an abstract syntactic tree of program source code and the data stream features, and training source code defect sorter, the method was arranged to achieve source code defect detection according to the deep learning algorithm. And then,analyzing the abstract syntactic tree of source code and the semantic and syntactic feature contained in the data stream, a general framework was proposed for deep learning based source code defect detection according to the key theories, deep learning algorithm and word nesting algorithm in nature language processing. Finally, an open data set SARD was used to validate the proposed method. The experimental results show that, the proposed method can learn semantic and syntactic features hidden in the source code and outperform the existing methods in terms of accuracy, recall rate, false positive rate, and false negative rate.