At　present,　the　construction　dust　has　caused　great　losses　to　people＇s　healthy　life　and　national　economic　development.　In　order　to　solve　the　shortcomings　of　the　existing　real-time　sensor　network　detection　of　construction　dust　such　as　poor　accuracy,　this　paper　proposes　an　automatic　identification　of　construction　dust　based　on　computer　vision　and　improved　K-Means　clustering　algorithm.　We　extract　the　saturation　of　HSV　color　model　of　each　image　to　form　a　text　data　set.　We　determine　the　median　value　of　the　data　set　as　the　initial　centroid　of　clustering,　reduce　the　number　of　iterations　and　get　the　global　optimal　solution.　Mahalanobis　distance　is　used　as　similarity　measure　to　cluster,　which　reduces　the　difference　between　different　feature　measures,　improves　the　accuracy,　and　realizes　the　automatic　recognition　of　construction　dust　based　on　computer　vision.　For　the　improved　K-Means　algorithm,　the　precision,　recall　rate　and　harmonic　mean　value　are　used　to　analyze　the　clustering　results.　The　experimental　results　show　that　the　improved　K-Means　algorithm　has　good　robustness　and　high　accuracy,　and　the　automatic　recognition　rate　can　reach　89.33%.　©　Content　from　this　work　may　be　used　under　the　terms　of　the　Creative　Commons　Attribution　3.0　licence.