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Driver fatigue driving is one of the major reasons of causing traffic accidents. This article puts forward a detection method of driver’s fatigue based on deep learning and facial multi-feature fusion. First, the Multi-task convolutional neural network to conduct face detection and feature point location, and makes use of 68 landmarks on the face of Dlib toolkit to extract the characteristic parameters of the driver's face; Secondly, it adds the different weights to get the parameter M based on the value of eye aspect ratio(EAR), percentage of eyelid closure over the pupil over time(PERCLORS) and mouth aspect ratio(MAR), and accumulates the frame number of M >0.605 in certain time to judge the degree of driver’s fatigue. Finally, the experimental results indicate that: this method can make effective use of video images to detect driver’s fatigue state in time, its accuracy and sensitivity are 93.1% and 90.2% respectively, which has great significance for driver protection and vehicle safety.