Abstract: Disclosed is a method for identifying driving fatigue based on a CNN-LSTM deep learning model including: collecting electroencephalograph signals of a subject during simulated driving; randomly issuing an operating command during simulated driving, and dividing the electroencephalograph signals into fatigue data and non-fatigue data according to a reaction time for the subject to complete the operating command; performing band-pass filtering and mean removal preprocessing on the electroencephalograph signals, and respectively extracting N minutes of fatigue electroencephalograph signal data and N minutes of non-fatigue electroencephalograph signal data to be detected; performing independent component analysis on the electroencephalograph signal data to remove interference signals; establishing a CNN-LSTM model and setting network parameters of the CNN-LSTM model; transmitting the electroencephalograph signal data with interference signals removed to a CNN network for feature extraction; and reshaping data of