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The traditional fatigue driving detection system generally adopts the method of identifying facial features and extracting information, which is easily interfered by external factors and has low detection efficiency. In response to this problem, an EEG fatigue detection system based on Deep Belief Network (DBM) was proposed. Combine the working principle of deep belief network and the overall framework of the system to design the hardware structure and software functions of the system. The digital signal is separated by the SAA7115 model signal decoder. The decoder is connected to the low-noise Video interface through the acquisition module circuit diagram to ensure that the separated EEG signal is a composite signal; the DSP digital signal processor of the TMS320DM642 is used to port 1 The signal is synthesized, and the composite signal of the port 2 signal is encoded to ensure that the signal acquisition is not interfered by external factors; the signal extracted by the limited Boltzmann machine in the hardware acquisition module is tested for fatigue degree, according to the intensity of the change of the EEG signal, Distinguish the characteristics of EEG signals under fatigue and unfatigued conditions, and complete the system design. The experimental results show that the designed system has high detection efficiency and can provide guarantee for the life safety of fatigue drivers.