Recommender　systems　based　on　graph　attention　networks　have　received　increasing　attention　due　to　their　excellent　ability　to　learn　various　side　information.　However,　previous　work　usually　focused　on　game　character　recommendation　without　paying　much　attention　to　items.　In　addition,　as　the　team　of　the　match　changes,　the　items　used　by　the　characters　may　also　change.　To　overcome　these　limitations,　we　propose　a　relation-aware　graph　attention　item　recommendation　method.　It　considers　the　relationship　between　characters　and　items.　Furthermore,　the　graph　attention　mechanism　aggregates　the　embeddings　of　items　and　analyzes　the　effects　of　items　on　related　characters　while　assigning　attention　weights　between　characters　and　items.　Extensive　experiments　on　the　kaggle　public　game　dataset　show　that　our　method　significantly　outperforms　previous　methods　in　terms　of　Precision,　F1　and　MAP　compared　to　other　existing　methods.　　©　2022　IEEE.