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At present, the recognition of traffic signs is based on the operating system, which cannot achieve autonomous control, stable and reliable. Based on this, a method of traffic sign recognition based on Microcontroller convolutional neural network was proposed. Considering the memory and calculation speed of the microcontroller, the research uses the improved SqueezeNet network model structure to reduce the weight of the traffic sign matrix files trained by the PC training machine by 50 times, and transplanted to the front-end Cortex-M core series development board; The embedded CMSIS-NN network function library is used to build the same network model structure as the training machine to realize fast recognition of the sign. The experimental results show that the average recognition rate of SqueezeNet traffic sign recognition method based on Microcontroller is over 97.4%, and the recognition speed is effectively improved. At the same time, it provides an alternative scheme for intelligent traffic sign recognition.