Concerning of the problems of high cost and unstable detection results of the traditional malicious code detection methods， a multi-neural network malicious code detection model based on depthwise separable convolution was proposed. By using the Depthwise Separable Convolution （DSC）， SENet （Squeeze-and-Excitation Network） channel attention mechanism and Grey Level Co-occurrence Matrix （GLCM）， three lightweight neural networks were connected with GLCM in parallel to detect malicious code families and their variants， then the detection results of multiple strong classifiers were fused via Naive Bayes classifier to improve the detection accuracy while reducing the computational cost. Experimental results on the hybrid dataset of MalVis + benign data show that the proposed model achieved the accuracy of 97.43% in the detection of malicious code families and their variants， which was 6.19 and 2.29 percentage points higher than those of ResNet50 and VGGNet models respectively， while its parameter quantity is only 68% of that of ResNet50 model and 13% of that of VGGNet model. On malimg dataset， the detection accuracy of this model achieved 99.31%. In conclusion， the proposed model has good detection effect with reduced parameters.