Color　classification　of　wooden　boards　is　helpful　to　improve　the　appearance　of　wooden　furniture　that　is　spliced　from　multiple　wooden　boards.　Due　to　the　similarity　of　colors　among　wooden　boards,　manual　color　classification　is　inaccurate　and　unstable.　Thus,　supervised　learning　algorithms　can　hardly　be　used　in　this　scenario.　Moreover,　wooden　boards　are　long,　and　their　images　have　a　high　resolution,　which　may　lead　to　the　growth　of　computational　complexity.　To　overcome　these　challenges,　in　this　paper,　we　propose　a　new　mechanism　for　color　classification　of　wooden　boards　based　on　machine　vision.　The　image　of　the　wooden　board　is　preprocessed　to　subtract　irrelevant　colors,　and　the　feature　vector　is　extracted　based　on　3D　color　histogram　to　reduce　the　computational　complexity.　In　the　offline　clustering,　the　feature　vector　sets　are　partitioned　into　different　clusters　through　the　K-means　algorithm.　Then,　the　clustering　result　can　be　used　in　the　online　classification　to　classify　the　new　wood　image.　Furthermore,　to　process　the　abnormal　images　of　wooden　boards,　we　propose　an　improved　algorithm　with　centroid　improvement　and　image　filtering.　The　experimental　results　verify　the　effectiveness　of　the　proposed　mechanism.