Relation　Extraction　systems　train　an　extractor　by　aligning　relation　instances　in　Knowledge　Base　with　a　large　amount　of　labeled　corpora.　Since　the　labeled　datasets　are　very　expensive,　Distant　Supervision　Relation　Extraction　(DSRE)　utilizes　rough　corpus　annotated　with　Knowledge　Graph　to　reduce　the　cost　of　acquisition.　Nevertheless,　the　data　noise　problem　limits　the　performance　of　the　DSRE.　Dependency　trees　can　be　used　to　filter　the　wrong-labeled　instances　in　the　distant　supervision　bag.　However,　existing　dependency　tree　relation　extraction　strategies　are　all　based　on　manually-set　paths　between　the　subject　and　object　entities,　and　suffer　from　the　problem　of　pruning　the　trees　too　aggressively　or　too　insufficiently.　To　circumvent　the　shortcomings,　in　this　paper,　we　propose　a　novel　DSRE　framework　A2DSRE,　based　on　the　Adaptive　dependency-path　and　Additional　KG　supervision.　To　obtain　the　dependency　paths　related　to　entity　relations　adaptively,　we　introduce　an　advanced　graph　neural　network-GeniePath　into　DSRE,　which　assigns　higher　weights　to　those　direct　neighbor　words　that　contribute　more　to　relation　prediction　through　breadth　exploration,　and　conducts　depth　exploration　to　determine　the　correlation　between　relations　and　high-order　neighbors.　In　this　way,　the　irrelevant　nodes　are　pruned　while　the　relevant　nodes　are　kept,　our　method　can　obtain　more　appropriate　paths　associated　with　relations.　At　the　same　time,　to　further　reduce　the　noises　in　the　data,　we　incorporate　additional　supervision　information　from　the　knowledge　graph　by　retracting　the　margin　between　the　representation　of　the　bag　and　the　pre-training　knowledge　graph　embedding.　Extensive　numerical　experiments　validate　the　effectiveness　of　our　new　method.