The　area　of　computer　vision　is　one　of　the　most　discussed　topics　amongst　many　scholars,　and　stereo　matching　is　its　most　important　sub　fields.　After　the　parallax　map　is　transformed　into　a　depth　map,　it　can　be　applied　to　many　intelligent　fields.　In　this　paper,　a　stereo　matching　algorithm　based　on　visual　sensitive　information　is　proposed　by　using　standard　images　from　Middlebury　dataset.　Aiming　at　the　limitation　of　traditional　stereo　matching　algorithms　regarding　the　cost　window,　a　cost　aggregation　algorithm　based　on　the　dynamic　window　is　proposed,　and　the　disparity　image　is　optimized　by　using　left　and　right　consistency　detection　to　further　reduce　the　error　matching　rate.　The　experimental　results　show　that　the　proposed　algorithm　can　effectively　enhance　the　stereo　matching　effect　of　the　image　providing　significant　improvement　in　accuracy　as　compared　with　the　classical　census　algorithm.　The　proposed　model　code,　dataset,　and　experimental　results　are　available　at　https://github.com/WangHewei16/Stereo-Matching.　©　2021　IEEE.