In　view　of　the　lack　of　interactive　information　between　aspect　words　and　the　context　in　most　aspect-level　sentiment　studies,　and　the　inability　to　make　full　use　of　semantic　information.　To　address　the　problems　above,　a　model　based　on　self-attention　and　graph　convolution　network　is　proposed.　In　order　to　improve　the　semantic　representation　ability　of　the　model,　the　multi-head　self-attention　mechanism　is　used　to　obtain　the　long-distance　dependency　relationship　of　the　text,　combined　with　the　dependency　type　matrix.　Then,　the　weight　matrix　that　combines　the　location　information　and　the　relationship　type　information　is　calculated　and　is　inputted　to　the　graph　convolution　network　to　obtain　the　text　feature　representation.　Besides,　the　text　aspect　attention　layer　is　employed　to　extract　the　context-sensitive　aspect　features,　and　it　is　inputted　to　graph　convolution　network　to　obtain　aspect　feature　representation.　Finally,　the　two　vectors　above　are　connected　to　complete　the　task　of　sentiment　analysis.　Simulation　results　show　that　the　overall　performance　of　the　proposed　model　is　better　than　that　these　of　other　comparison　models　in　two　open　datasets.　©　2024　Beijing　University　of　Posts　and　Telecommunications.　All　rights　reserved.