With the increase of car ownership and the complex and crowded parking environment, it is difficult for drivers to complete the parking operation quickly and accurately, which may cause traffic accidents such as vehicle collisions and road jams because of poor parking skills. The emergence of an automatic parking system can help drivers park safely and reduce the occurrence of safety accidents. In this paper, the neural network identifier on the control method of an adaptive integral derivative of a neural network is proposed for an automatic parallel parking system with front-wheel steering is studied by using MATLAB/Simulink environment, and the simulation is carried out. Firstly, according to vehicle parameters and obstacle avoidance constraints, the minimum parking space, and parking starting position are calculated. Meanwhile, the path planning of parallel parking spaces is carried out by quintic polynomial. The fuzzy control algorithm and neural network algorithm are used to realize automatic parking. Finally, the pre-operation, decision-making speed, correlation coefficient between input data and output data of the two algorithms are compared. Fuzzy control needs to establish a fuzzy rule base, while the neural network needs a lot of data training so that the two control algorithms can complete automatic parking. The decision-making speed of the neural network algorithm is faster than that of the fuzzy control algorithm, and the correlation coefficient is larger. At the same time, the generalization ability of the neural network algorithm is better, and the requirement of the initial position and posture of the vehicle is lower.