Semantic　segmentation　is　one　of　the　fundamental　tasks　in　understanding　and　applying　urban　scene　point　clouds.　Recently,　deep　learning　has　been　introduced　to　the　field　of　point　cloud　processing.　However,　compared　to　images　that　are　characterized　by　their　regular　data　structure,　a　point　cloud　is　a　set　of　unordered　points,　which　makes　semantic　segmentation　a　challenge.　Consequently,　the　existing　deep　learning　methods　for　semantic　segmentation　of　point　cloud　achieve　less　success　than　those　applied　to　images.　In　this　article,　we　propose　a　novel　method　for　urban　scene　point　cloud　semantic　segmentation　using　deep　learning.　First,　we　use　homogeneous　supervoxels　to　reorganize　raw　point　clouds　to　effectively　reduce　the　computational　complexity　and　improve　the　nonuniform　distribution.　Then,　we　use　supervoxels　as　basic　processing　units,　which　can　further　expand　receptive　fields　to　obtain　more　descriptive　contexts.　Next,　a　sparse　autoencoder　(SAE)　is　presented　for　feature　embedding　representations　of　the　supervoxels.　Subsequently,　we　propose　a　regional　relation　feature　reasoning　module　(RRFRM)　inspired　by　relation　reasoning　network　and　design　a　multiscale　regional　relation　feature　segmentation　network　(MS-RRFSegNet)　based　on　the　RRFRM　to　semantically　label　supervoxels.　Finally,　the　supervoxel-level　inferences　are　transformed　into　point-level　fine-grained　predictions.　The　proposed　framework　is　evaluated　in　two　open　benchmarks　(Paris-Lille-3D　and　Semantic3D).　The　evaluation　results　show　that　the　proposed　method　achieves　competitive　overall　performance　and　outperforms　other　related　approaches　in　several　object　categories.　An　implementation　of　our　method　is　available　at:　https://github.com/HiphonL/MS_RRFSegNet.