In big data environment, traditional malicious file detection methods have low detection accuracy for malicious files after code variant and confusion, and weak versatility of cross-platform malicious files. To resolve these problems, a malicious file detection method based on image texture and Convolutional Neural Network (CNN) was proposed. Firstly, a grayscale image generation algorithm was used to convert the executable files on Android and Windows platforms, namely.dex and.exe files, into corresponding grayscale images. Then, the texture features of these grayscale images were automatically extracted and learned by using CNN algorithm, to construct a malicious file detection model. Finally, a large number of unknown files were used to test the accuracy of the proposed model. The experimental results on a large number of malicious samples showed that the highest accuracy of the proposed model on Android platform and Windows platform reached 79.6% and 97.6%, and the average accuracy were approximately 79.3% and 96.8%, respectively. Compared with the texture fingerprint-based malicious code detection method, the accuracy of the proposed method was improved by about 20%. Experimenatal results indicate that the proposed method can effectively avoid the problems caused by manual screening features, greatly improve the detection accuracy and efficiency, successfully solve the cross-platform detection problem, and achieve an end-to-end malicious file detection model.