Infrared　small　target　detection　technology　is　one　of　the　key　technologies　for　reconnaissance,　guidance,　and　early　warning　systems,　and　it　has　important　theoretical　and　practical　value　to　conduct　in-depth　research　on　it.　However,　there　are　several　challenges　in　infrared　small　target　detection.　Firstly,　infrared　small　targets　have　low　signal-to-noise　ratio,　which　makes　them　easily　submerged　in　complex　backgrounds.　Secondly,　since　infrared　small　target　detection　is　a　long-distance　imaging　process,　there　is　no　shape　or　texture　information　available,　which　increases　the　difficulty　of　target　detection.　To　address　these　challenges,　this　paper　proposes　a　multi-level　contrast　enhancement　method　to　suppress　structural　background,　and　develops　a　more　effective　detection　algorithm.　Based　on　the　concept　of　local　contrast　measurement　(LCM),　a　new　contrast-based　small　target　detection　algorithm　called　Multi-Level　Local　Contrast　Measurement　(MLLCM)　is　constructed,　and　its　effective　implementation　process　is　provided.　Compared　with　LCM,　MPCM(Multiscale　Patch-based　Contrast　Measure),　and　other　algorithms,　this　algorithm　effectively　enhances　the　target　area　and　eliminates　background　clutter.　The　results　on　simulated　images　demonstrate　the　effectiveness　of　this　algorithm.　©　2023　The　Authors.　Published　by　Elsevier　B.V.　This　is　an　open　access　article　under　the　CC　BY-NC-ND　license　(https://creativecommons.org/licenses/by-nc-nd/4.0)　Peer-review　under　responsibility　of　the　scientific　committee　of　the　Tenth　International　Conference　on　Information　Technology　and　Quantitative　Management.