Machine vision inspection combines a fast and accurate computer with image processing technology, which is of great help to improve the ability of intelligent image recognition. The ultimate goal of the recognition and classification system is to identify the detected defects quickly and accurately. The defects with obvious characteristics can be identified in a short time due to their great differences. Due to the imperfect recognition technology and the low robustness of the algorithm, the detection is inaccurate and the difficulty of the recognition effect is increased. In this paper, based on the targets with similar characteristics, the membership function of the target to be recognized is constructed after extracting the characteristic parameters of the recognized target. The characteristic function of the fuzzy set is established to calculate the membership degree of the target sample belonging to the fuzzy set, and finally, the classification of the characteristic sample is recognized according to the maximum membership principle. The experimental results show that the method of this paper is based on pattern recognition and has good results, which has guiding significance for all kinds of image feature recognition and processing and will greatly reduce the intensity and workload of manual processing.