The　category　of　road　marking　is　a　crucial　element　in　Mobile　laser　scanning　systems＇　(MLSs)　applications　such　as　intelligent　traffic　systems,　high-definition　maps,　location　and　navigation　services.　Due　to　the　complexity　of　road　scenes,　considerable　and　various　categories,　occlusion　and　uneven　intensities　in　MLS　point　clouds,　finely　road　marking　classification　is　considered　as　the　challenging　work.　This　paper　proposes　a　graph　attention　network　named　GAT_SCNet　to　simultaneously　group　the　road　markings　into　11　categories　from　MLS　point　clouds.　Concretely,　the　proposed　GAT_SCNet　model　constructs　serial　computable　subgraphs　and　fulfills　a　multi-head　attention　mechanism　to　encode　the　geometric,　topological,　and　spatial　relationships　between　the　node　and　neighbors　to　generate　the　distinguishable　descriptor　of　road　marking.　To　assess　the　effectiveness　and　general-ization　of　the　GAT_SCNet　model,　we　conduct　extensive　experiments　on　five　test　datasets　of　about　100　km　in　total　captured　by　different　MLS　systems.　Three　accuracy　evaluation　metrics:　average　Precision,　Recall,　and　F-1　of　11　categories　on　the　test　datasets　exceed　91%,　respectively.　Accuracy　evaluations　and　comparative　studies　show　that　our　method　has　achieved　a　new　state-of-the-art　work　on　road　marking　classification,　especially　on　similar　linear　road　markings　like　stop　lines,　zebra　crossings,　and　dotted　lines.