Relation　prediction　aims　to　infer　the　missing　relations　among　entities　in　knowledge　graphs,　where　inductive　relation　prediction　enjoys　great　popularity　due　to　its　effectiveness　to　be　applied　to　emerging　entities.　Most　existing　approaches　learn　the　logical　compositional　rules　or　utilize　subgraphs　to　predict　the　missing　relation.　Although　great　progress　has　been　made　in　the　performance,　current　models　are　still　suboptimal　due　to　their　limited　ability　to　capture　topological　information　that　is　critical　for　local　relation　prediction.　To　address　this　problem,　we　propose　a　novel　inductive　relation　prediction　approach　called　substructure-aware　subgraph　reasoning　which　incorporates　the　substructure　information　of　subgraphs　into　the　reasoning　process,　thus　making　the　relation　prediction　more　precise.　Specifically,　we　extract　the　entities　and　relations　around　the　target　entities　to　form　the　subgraph　and　then　encode　the　structure　information　of　nodes　and　edges　by　counting　the　number　of　certain　substructures.　Next,　the　structural　information　is　explicitly　applied　to　the　message　passing　for　more　accurate　reasoning.　To　improve　the　performance,　we　also　utilize　the　semantic　correlations　between　relations　as　auxiliary　information.　Experimental　results　on　three　benchmark　datasets　show　the　effectiveness　of　the　proposed　approach　for　the　inductive　relation　prediction.