The rapid growth of malicious applications has brought a huge security threat to mobile intelligent terminals. It is of great significance to achieve high-precision detection of malicious applications for mobile network information security. In view of this, this paper proposes a malicious application detection method based on improved deep residual shrinkage network. Firstly, the traffic features are preprocessed into convolutional neural network inputs, and then the channel attention mechanism and spatial attention mechanism are introduced to weight the sample features from the channel and spatial dimensions. Then, the deep residual shrinkage network is introduced to adaptively filter out the redundant features of the samples, and the parameters are back propagated through the identical connection optimization, so as to reduce the difficulty of model training and classification, and finally realize the high-precision identification of Android malicious applications. The proposed method can avoid manual feature extraction, achieve high-precision classification and has certain generalization ability. Experimental results show that the accuracy of the proposed method is 99.40%, 99.95% and 97.33% in 2-classification, 4 -classification and 42-classification of malicious applications, which is 0.21%, 2.65% and 25.85% higher than the existing methods.Experimental results show that the accuracy of the proposed method in 2-classification, 4-classification and 42-classification of malicious applications is 99.40%, 99.95% and 97.33% respectively. Compared with the existing methods, the proposed method has higher classification performance and generalization ability.