To improve the accuracy, anti-interference and real-time performance of linear motor mover position measurement from the shooting target image, an image displacement measurement algorithm based on fine interpolation of correlation peak (FICP) is introduced in this paper, and a deep learning algorithm is used to select the fence image with strong robustness. First, the width standard deviation and average gray gradient of the fence fringe are controlled to generate a series of fence fringe images. Second, the displacement of adjacent target images is calculated by combining chirp Z transform and FICP algorithm. Then, the mean error of displacement estimation is used as the evaluation index, and deep neural network is used to establish the quality optimization model of fence image, and the aperiodic fence image with strong robustness is screened out. Finally, the one-dimensional fence image signal in the motion process is obtained by line-scanning camera, the calibration coefficient of the system is determined according to the chessboard target method, and the actual displacement value is obtained. Simulation and experimental results show that the optimized aperiodic fence image selected in this paper can effectively improve the measurement accuracy and prove the correctness of this method.