The invention discloses a vehicle insurance risk control pricing model method based on deep learning, and the method comprises the following steps: 1, a user inputs vehicle information, including vehicle element information such as a license plate number, a brand, a model, a vehicle initial registration date and the like; 2, the system automatically complements the information such as the frame number and the VIN code, and a professional worker checks the correctness of the information such as the frame number and the VIN code; 3, the user inputs the mileage of the vehicle, and checks the mileage of the vehicle by a special worker; 4, in consideration of information security, fields in the middle need to be subjected to desensitization display. According to the invention, data drilling is carried out around four dimensions of "people, vehicles, behaviors and historical compensation", through a big data analysis technology and a machine learning algorithm, the risk of a vehicle accident is accurately predicted in an omnibearing and three-dimensional manner, a "risk score" is generated, and the higher the score is, the higher the compensation risk is, so that vehicle insurance risk control pricing is more reasonable.