Relation　extraction　is　fundamental　in　knowledge　graph　construction.　Recent　studies　have　indicated　the　efficacy　of　Graph　Convolutional　Networks　in　enhancing　performance　in　relation　extraction　tasks　by　leveraging　dependency　trees.　However,　noise　in　automatically　generated　dependency　trees　poses　a　challenge　to　using　syntactic　dependency　information　effectively.　In　this　paper,　we　propose　an　Adaptive　Graph　Attention　Network　model　based　on　Dependency　Type　and　Direction　information,　which　effectively　reduces　noise　through　direction-aware　dependency　relations,　thereby　enhancing　extraction　performance.　Specifically,　we　propose　an　adaptive　graph　attention　mechanism　to　construct　direction-aware　adjacency　matrices　for　the　precise　aggregation　of　dependency　pairs　centred　around　entities　as　head　words.　This　mechanism　effectively　filters　out　noise　interference　from　entities　acting　as　dependent　words.　Moreover,　our　model　dynamically　allocates　weights　to　different　dependency　types　based　on　their　directions.　This　adaptive　allocation　enhances　the　learning　capability　of　entity　representations,　optimizing　the　encoding　and　extraction　of　entity　information.　Experimental　results　on　two　English　benchmark　datasets　demonstrate　that　introducing　direction　information　significantly　enhances　the　model’s　performance.　These　findings　validate　the　efficacy　of　incorporating　directionality　in　encoding　to　reduce　dependency　noise　and　improve　relation　extraction　task　performance.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Switzerland　AG　2024.