Existing deep learning-based malicious code detection methods have problems such as weak deep-level feature extraction capability, relatively complex model and insufficient model generalization capability. At the same time, code reuse phenomenon occurred in large number of malicious samples of the same type, resulting in similar visual features of the code. This similarity can be used for malicious code detection. Therefore, a malicious code detection method based on multi-channel image visual features and AlexNet was proposed. In the method, the codes to be detected were converted into multi-channel images at first. After that, AlexNet was used to extract and classify the color texture features of the images, so as to detect the possible malicious codes. Meanwhile, the multi-channel image feature extraction, the Local Response Normalization(LRN) and other technologies were used comprehensively, which effectively improved the generalization ability of the model with effective reduction of the complexity of the model. The Malimg dataset after equalization was used for testing, the results showed that the average classification accuracy of the proposed method was 97.8%, and the method had the accuracy increased by 1.8% and the detection efficiency increased by 60.2% compared with the VGGNet method. Experimental results show that the color texture features of multi-channel images can better reflect the type information of malicious codes, the simple network structure of AlexNet can effectively improve the detection efficiency, and the local response normalization can improve the generalization ability and detection effect of the model.