Feature　extraction　algorithm　plays　an　important　part　in　content　based　pornographic　image　recognition.　In　this　paper,　the　performances　of　six　outstanding　local　feature　extraction　algorithms　are　compared　and　analysed　in　Web　pornographic　image　recognition.　The　six　algorithms　include　Scale　Invariant　Feature　Transform,　Speeded　Up　Robust　Features,　Oriented　FAST　and　Rotated　BRIEF,　Fast　Retina　Keypoint,　Binary　Robust　Invariant　Scalable　Keypoints　and　KAZE.　Through　the　comparison　experiments　based　on　the　same　image　recognition　scheme,　we　conclude　that　the　highest　recognition　precision　can　be　obtained　by　SURF,　and　a　good　trade-off　between　recognition　speed　and　precision　can　be　achieved　by　ORB.