In　the　aspect-based　sentiment　analysis　task,　the　syntactic　dependency　parsing　of　the　text　is　required　first,　which　is　highly　dependent　on　the　quality　of　the　dependency　parsing　and　does　not　take　into　account　the　lack　of　correlation　between　dependency　parsing　and　semantic　knowledge.　Therefore,　a　two-channel　graph　convolution　model　based　on　syntactic　perception　and　knowledge　enhancement　is　proposed　for　the　aspect-based　sentiment　analysis　task.　Syntax-perception　mechanisms　are　used　to　learn　sentence　dependencies　in　one　channel,　and　knowledge　enhancement　is　performed　in　the　other　channel　through　a　knowledge　graph,　with　the　Outputs　of　the　two　Channels　correlated　through　an　Information　interaction　mechanism,　which　allows　the　model　to　pay　more　syntactic　and　semantic　attention　to　important　words　associated　with　aspectual　words.　In　addition,　a　positional　attention　mechanism　is　introduced　to　adjust　the　score　weights　of　words　with　respect　to　the　position,　which　in　turn　improves　the　Performance　of　the　aspect-based　sentiment　analysis　task.　Experiments　are　conducted　on　three　public　datasets,　Restl4,　Lapl4　and　Twitter.　Compared　to　other　aspect-based　sentiment　analysis　models,　this　paper＇　s　model　shows　a　more　significant　improvement　in　both　accuracy　and　Fl　value.　Experiments　show　that　syntactic　perception　and　knowledge　enhancement　can　guide　the　graph　convolutional　model　to　perform　deeper　semantic　learning　and　reasonable　weight　allocation,　thus　improving　the　Performance　of　aspect-based　sentiment　analysis　tasks.　©　2024　Science　Press.　All　rights　reserved.