In order to solve the problem of surveillance video image vehicle type recognition under the expressway environment, it is necessary to upload massive video data to the computer server center and structured processing vehicle type recognition for all video stream images, which causes great pressure on the data in the server center, heavy computing tasks and high requirements on the server performance. Based on this, a method of Cortex-M surveillance video vehicle type recognition based on machine learning was used to investigate. Firstly, the weight matrix files of the six models trained by the training machine were transplanted to the front-end Cortex-M core series development board and the CMSIS-NN network function library embedded in the development board was used to build the same network model structure as the training machine; At the same time, the CMSIS-DSP library embedded in the development board was used to speed up the image processing speed and the vehicle type recognition was realized for the surveillance video image of the selected processing. The experimental results show that the average recognition rate of the method is above 94.6%, which is the same as the recognition by computer. It is concluded that this method relieves the pressure caused by a large number of video uploads to the server center and provide an alternative solution for surveillance video image vehicle type recognition in the highway environment.