Missing　values　often　exist　in　scientific　datasets.　Therefore,　practical　methods　for　missing　data　imputation　and　classification　are　necessary　for　machine　learning,　data　analysis.　The　k-Nearest　Neighbor　(KNN)　algorithm　is　a　simple　and　effective　algorithm　in　missing　data　imputation　and　classification.　This　paper　focuses　on　the　missing　data　classification　problem　and　proposes　a　new　classification　method　based　on　the　local　mean　k-nearest　centroid　neighbour.　When　making　classification　judgments,　the　proposed　method　examines　the　closeness　and　symmetrical　arrangement　of　the　k　neighbours　and　adopts　the　local　mean-based　vector　of　the　k　centroid　neighbours　for　each　class.　We　run　classification　error　experiments　on　six　UCI　datasets　to　see　how　well　the　proposed　method　performs　when　there　is　missing　data.　Experimental　results　show　that　the　performance　of　our　proposed　method　obtains　a　significant　improvement　compared　to　the　most　advanced　KNN-based　algorithms.　©　2022　ACM.