Point　clouds　semantic　labeling　is　an　important　task　in　3D　computer　vision.　Current　major　researches　focus　on　fully　supervised　learning.　However,　point-by-point　manual　annotations　are　expensive　and　time-consuming.　To　this　end,　we　propose　a　general　point　clouds　deep　active　learning　framework　to　ease　the　annotation　burden　for　researchers.　In　this　work,　we　propose　a　conditional　random　field　(CRF)　based　pseudo　labels　generation　to　provide　more　supervised　information　for　deep　neural　network　(DNN)　and　employ　Golden　Loss　Correction　(GLC)　to　correct　pseudo　labeled　data　training　loss.　Finally,　we　propose　the　Modified　Margin　acquisition　function　which　can　select　the　most　valuable　points　for　labeling.　We　demonstrate　the　improvements　provided　by　our　proposed　method　on　the　S3DIS　benchmark.