The　existing　aspect　level　sentiment　analysis　methods　cannot　solve　the　problem　of　polysemous　word　in　different　contexts.　Therefore,　a　method　for　aspect　level　sentiment　analysis　based　on　knowledge　graph　and　recurrent　attention　network　is　proposed.　The　text　representation　of　the　bidirectional　long　short-term　memory　network　is　integrated　with　synonym　information　in　knowledge　graph　using　dynamic　attention　mechanism　to　obtain　the　state　vector　of　knowledge　perception.　To　classify　aspect　level　sentiment,　the　memory　content　is　constructed　by　combining　the　location　information　and　inputting　the　multi-level　gated　recurrent　unit　for　calculating　the　sentiment　characteristics　of　aspect　terms.　The　experimental　results　show　that　the　proposed　method　achieves　better　classification　results　on　three　open　datasets.　©　2020,　Science　Press.　All　right　reserved.