Aiming at the problems of poor robustness of fatigue driving detection system and too simple classification of fatigue degree, use MediaPipe face key point detection technology and a fuzzy inference system to fuse a variety of facial fatigue features to study the quantitative assessment method of driver fatigue degree in video sequences and realize realtime scoring of driver fatigue degree and fatigue warning. In this paper, firstly, the MediaPipe face detection model is used to locate the facial key points; secondly, the detected key points are used to extract the dynamic features of facial fatigue from the video frames, and four evaluation indexes are obtained: PERCLOS, yawn length, whether or not to nod off, and the approximate entropy of face oscillation; finally, the fuzzy inference system is designed to quantify the fatigue degree and realize the real-time evaluation of driver fatigue. The proposed method shows that the proposed method is scientifically sound. The study shows that the proposed method scientifically and effectively achieves the quantitative assessment of driver fatigue degree and further improves the robustness and reliability of fatigue driving detection based on facial features.