For Android malware detection, Most of research proposed multi-type features combined with machine learning to improve the detection rate of malware detection, but rarely considered association between application interface and edge information in call graph. This paper designs a method of Android malware detection based on accessibility feature of application interface. This method extracts accessibility features of application interface based on malicious behaviors, effectively makes feature set contain edge information. Experiments were conducted on 1151 malware collected by VirusShare in 2018 and 1021 benign programs from Google player. Experiments show that random forest is more effective than other four methods in malware detection, and accuracy of model reaches 98.90%. Results show that accessibility features improved recall rate and precision of the model , and is more important than other features in the experiment.