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In order to reduce the operating burden of drivers in the process of driving and thus reduce the probability of error judgment events, a driving assistance system based on convolutional neural network is designed. In a well-executed automobile Navigation architecture, the connection position between the Learning Navigation module and the Learning Controller module is defined. Then, according to the collection of driving pictures by the auxiliary driving sensors, the directional control of vehicle cruising ability is carried out to suppress the transition vibration of auxiliary waves in the monitoring instrument, so as to complete the requirements and design analysis of the driving assistance system. On this basis, the auxiliary activation function and the driving image in the instrument are determined, and a standardized convolutional neural network is established. According to the learning results of driving assistance data, it is transmitted and processed, and then the Job request of driving assistance system is connected to realize the smooth operation of the system. The experimental results of real vehicle design using convolutional neural network platform show that the minimum vibration amplitude of auxiliary wave in vehicle monitoring instrument is between 36-61hz after the application of driving assistance system, and the average wavelength offset is significantly reduced, thus effectively relieving the operating burden of the driver.