In　processing　images,　the　detection　and　matching　of　feature　points　occupy　an　extremely　critical　position.　Considering　the　actual　application　scenarios　in　image　recognition　and　processing　problems,　this　paper　designs　experiments　on　the　traditional　SIFT　(Scale-invariant　feature　transform),　SURF　(Speeded　Up　Robust　Features),　ORB　(Oriented　FAST　and　Rotated　BRIEF)　three　algorithms　from　the　four　aspects　of　algorithm　execution　speed,　lighting　conditions,　rotation　processing,　and　blurred　pixels,　and　compares　the　experimental　results.　We　have　the　following　conclusions:　the　ORB　algorithm　has　a　faster　execution　speed,　and　the　ratio　of　execution　speed　of　SIFT,　SURF,　ORB　algorithm　is　about　2:1:14.　In　terms　of　feature　point　matching,　the　advantage　of　SIFT　algorithm　is　more　prominent,　and　it　can　still　show　a　higher　matching　rate　in　three　complex　situations.　SURF　is　the　most　robust　algorithm.　Even　in　the　simulation　of　various　complex　image　transformation　scenarios,　it　also　performs　better,　SIFT　comes　next,　and　the　ORB　algorithm　has　the　worst　robustness.　©　2021　IEEE.