To recognize driver′s fatigue level accurately and objectively, a driver fatigue level recognition model that employs multiple psychological features was developed based on particle swarm optimization (PSO) and support vector machine (SVM). Firstly, the driver fatigue was divided into four levels, which were alert, mild fatigue, deep fatigue and drowsiness. Then alpha rhythm, beta rhythm, delta rhythm, mean of heart rate, and standard deviation of R-R interval were selected as input variables of the SVM model. The PSO was introduced into the model to optimize the penalty parameter and kernel function parameter of SVM. Experimental data collected in Ji-Hun freeway was used to validate the effectiveness of the recognition model. Results show that the recognition precision of the four fatigue levels are higher than 85％. Sensitive analysis of the mode is also conducted and the results prove that the model using multiple features outperforms the model using fewer features.