With the increasing variety and quantity of Android malware， it becomes increasingly important to detect malware to protect system security and user privacy. To address the problem of low classification accuracy of traditional malware detection models， A malware detection model for Android named CBAM-CGRU-SVM was proposed based on Convolutional Neural Network （CNN）， Gated Recurrent Unit （GRU）， and Support Vector Machine （SVM）. In this model， more key features of malware were learned by adding a Convolutional Block Attention Module （CBAM） to the convolutional neural network， and GRUs were employed to further extract features. In order to solve the problem of insufficient generalization ability of the model when performing image classification， SVM was used instead of softmax activation function as the classification function of the model. Experiments were conducted on Malimg public dataset， in which the malware data was transformed to images as model input. Experimental results show that the classification accuracy of CBAM-CGRU-SVM model reaches 94.73%， which can effectively classify malware families.