As software becomes more complex, the need for research on vulnerability detection is increasing. The rapid discovery and patching of software vulnerabilities is able to minimize the damage caused by vulnerabilities. As an emerging detection method, deep learning-based vulnerability detection methods can learn from the vulnerability code and automatically generate its implied vulnerability pattern, saving a lot of human effort. However, deep learning-based vulnerability detection methods are not yet perfect; function-level detection methods have a coarse detection granularity with low detection accuracy; slice-level detection methods can effectively reduce sample noise, but there are still the following two aspects of the problem: On the one hand, most of the existing methods use artificial vulnerability datasets for experiments, and the ability to detect vulnerabilities in real environments is still in doubt; on the other hand, the work is only dedicated to detecting the existence of vulnerabilities in the slice samples and the lack of interpretability of the detection results. To address above issues, this paper proposes a slice-level vulnerability detection and interpretation method based on the graph neural network. The method first normalizes the C/C++ source code and extracts slices to reduce the interference of redundant information in the samples; secondly, a graph neural network model is used to embed the slices to obtain their vector representations to preserve the structural information and vulnerability features of the source code; then the vector representations of slices are fed into the vulnerability detection model for training and prediction; finally, the trained vulnerability detection model and the vulnerability slices to be explained are fed into the vulnerability interpreter to obtain the specific lines of vulnerability code. The experimental results show that in terms of vulnerability detection, the method achieves an F1 score of 75.1% for real-world vulnerability, which is 41.2%-110.4% higher than the comparative methods. In terms of vulnerability interpretation, the method can reach 73.6% accuracy when limiting the top 10% of critical nodes, which is 8.9% and 24.9% higher than the other two interpreters, and the time overhead is reduced by 42.5% and 15.4%, respectively. Finally, this method correctly detects and explains the real vulnerabilities in the four open source software that match the 36 existing vulnerability patterns, proving its practicality in real-world vulnerability discovery.