As the detection result lacks interpretability, the Android malware detection is analyzed in terms of interpretability. This study proposes an interpretable Android malware detection method (multilayer perceptron attention method, MLP_At) comprehensively using the multilayer perceptron and attention mechanism. By extracting permissions and application programming interface (API) features from Android malware, it performs data preprocessing on the proposed features to generate feature information, and multilayer perceptrons are utilized for learning features. Finally, the learned data is classified by the BP algorithm. The attention mechanism is introduced in the multilayer perceptron to capture sensitive features and generate descriptions based on the sensitive features to explain the core malicious behavior of the application. The experimental results show that the proposed method can effectively detect malware and the accuracy is improved by 3.65%, 3.70%, and 2.93% compared with that of SVM, RF and XGBoost, respectively. The method can accurately reveal the malicious behavior of the software and can also explain the reasons why samples are misclassified.