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In order to avoid the influence of the same direction and the opposite direction interference signals on the recognition accuracy, machine learning is introduced to study the signal recognition system of the active anti-collision early warning radar for vehicles. Under the support of machine learning, the overall architecture of the early warning radar signal recognition system is designed. The original bgt24mtr12e6327xuma1 radar transceiver is used, and the mixing signal is sent to the signal processing system through the tendaa9 signal amplifier to control the vehicle speed. TMS320F206 DSP connects external equipment and tja1041a bus transceiver through CAN bus, which enables serial communication between PC and DSP. Based on machine learning, the interference free real-time state signal is obtained by using the anti-interference pipeline structure conversion method. By calculating the radar signal similarity, the specific recognition process is designed. The experiment is designed according to the distribution of each sub radar on the vehicle. According to the experimental results, the recognition accuracy of machine learning technology signal can reach 96% at most under the condition of the opposite interference; the recognition accuracy of machine learning technology signal can reach 94% at most under the condition of the same interference, providing equipment support for the safe driving of the vehicle.