Vehicle type recognition has become critical in intelligent transportation systems. The existing technology about vehicle type recognition is difficult to balance the recognition accuracy and recognition speed. Aiming at the problem of vehicle type recognition in the highway environment, a layered broad model combining the shallow feature layer with the broad feature layer is proposed, which can recognize the vehicle in real time. Firstly, the combination of color space conversion and multi-channel HOG algorithm is used to reduce the influence of illumination environment and realize the feature extraction of vehicle image. Combined with PCA dimension reduction technology, the computational complexity is reduced. Then sparse representation and nonlinear mapping of image features reduce correlation between image features. Finally, the ridge regression learning algorithm is used to train the extracted sample features, and the weight coefficient between the sample features and the sample tags is obtained to realize the recognition of the vehicle type. Experimental results on the BIT-Vehicle database show that the recognition accuracy of the proposed method is 96.69%, and the recognition speed is as high as 70.3 fps. The proposed algorithm can effectively enhance the feature expression ability and improve the vehicle type recognition accuracy, and ensure the real-time performance, which is superior to other algorithms in recognition accuracy and speed.