In order to solve the shortcomings of current fatigue detection methods such as low accuracy or poor real-time performance, a fatigue detection method based on multi-feature fusion is proposed. Firstly, the HOG face detection algorithm and KCF target tracking algorithm are integrated and deformable convolutional neural network is introduced to identify the state of extracted eyes and mouth, fast track the detected faces and extract continuous and stable target faces for more efficient extraction. Then the head pose algorithm is introduced to detect the driver"s head in real time and obtain the driver"s head state information. Finally, a multi-feature fusion fatigue detection method is proposed based on the state of the eyes, mouth and head. According to the experimental results, the proposed method can detect the driver"s fatigue state in real time with high accuracy and good robustness compared with the current fatigue detection algorithms.