As one of the important ways to record life and store informatio n,image is a reproduction of the material of human visual perception,and at the same time a true portrayal of real scenes.In the face of massive image data,how to accurately and efficientl y extract image features,obtain useful information,and transform the information into the features is a problem to solve.In response to this problem,the CSLBP-based fuzzy image feature extraction and detection method proposed in this paper solves this problem well. At the same time, it combines HOG feature extraction and detection,Haar feature extraction and de tection,and deep learning-based face feature detection methods to compare with the algorithm in this paper,and analyze the basic principles,steps,and applications of various methods.Second ly,the methods that have emerged in recent years are introduced,the classic methods and new me thods are analyzed and compared from the perspective of problem solving,the existing prob lems are compared,summarized,and conclusions are drawn.It turns out that the fuzzy ima ge feature matching algorithm proposed by the algorithm in this chapter has certain advanta ges over classic algorithms and deep learning algorithms,but they all have improvements.For exa mple,how to control the feature extraction rate under high noise conditions,and how to improve the stability underlight intensity,this will bethe focus of the future research.