Micro　stereovision　based　on　stereo　light　microscope　(SLM)　is　used　in　Micro　stereo　measurement,　in　which　stereo　matching　is　an　important　part.　In　this　paper,　a　new　method　for　solving　this　problem　is　proposed　by　raising　an　optimized　stereo　matching　algorithm　based　on　Hopfield　Neural　network　and　color　stereo　image　pair　getting　from　SLM.　In　this　method　we　firstly　define　the　color　similarity　by　using　the　internal　association　of　RGB　in　a　color　image,　and　then　combine　it　with　Rank　transform　and　the　NCC　algorithm　to　build　the　optimal　stereo　matching　relations.　At　last,　we　use　these　relations　to　build　the　Energy　function　of　Hopfield　network　combined　with　the　inherent　constraints　of　computer　stereo　vision.　The　experimental　results　show　that　the　accuracy　of　stereo　matching　and　noise　immunity　are　partially　improved　by　using　Hopfield　network　algorithm　compared　to　NCC　algorithm.