The present invention relates to a vehicle-borne multi-obstacle classification device and method based on a Bayes classifier. The classification device comprises a camera and a PC which is connected to the camera. The PC comprises a Kalman filter module for carrying out Kalman filtering on the vehicle front video image collected by a camera and detecting an obstacle target, a characteristic extraction module which is used for carrying out characteristic extraction on the detected obstacle target, and a Bayes classification module which is used for using a Naive Bayes classifier to obtain the classification of the obstacle target according to the characteristics of the obstacle target, wherein the characteristics comprise a symmetry characteristic, a horizontal edge straightness characteristic and a length and width ratio characteristic, and the classification comprises a cyclist / motorcycle rider, a vehicle side face, a vehicle front side and pedestrians. Compared with the prior art, the device and the method have the advantages of high recognition accuracy, strong anti-interference ability, high efficiency, and good real-time performance.