Since the release of Android, it has become the most widely used mobile phone operating system in the world due to its advantages such as open source, rich hardware, and diverse application markets. At the same time, the explosive growth of Android devices and Android applications (app for short) has made it a target of 96% of mobile malware. Among current detection methods, the direct extraction of simple program features, ignoring the program semantics is fast but less accurate, and the conversion of semantic information of programs into graph models for analysis improves accuracy but has high runtime overhead and is not very scalable. To address these challenges, the program semantics of an App is distilled into a function call graph and the API call is abstracted to convert the call graph into a simpler graph. Finally, these vectors are fed into a graph convolution network (GCN) model to train a classifier with triplet loss (i.e., SriDroid). After conducting experimental analysis on 20 246 Android apps, it is found that SriDroid can achieve 99.17% malware detection accuracy with sound robustness.