Graph　Convolutional　Networks　(GCNs)　have　garnered　significant　attention　in　recent　years,　finding　applications　across　various　domains,　including　recommendation　systems,　knowledge　graphs,　and　biological　prediction.　One　prominent　GCN-based　recommendation　model,　LightGCN,　optimizes　embeddings　for　final　prediction　through　graph　convolution　operations,　and　has　achieved　outstanding　performance　in　commodity　recommendation　and　molecular　property　prediction.　However,　LightGCN　suffers　from　suboptimal　layer　combination　parameters　and　limited　nonlinear　modeling　capabilities　on　the　software　side.　On　the　hardware　side,　due　to　the　irregularity　of　the　aggregation　phase　of　LightGCN,　CPU　and　GPU　executions　are　not　efficient,　and　designing　a　accelerator　will　be　constrained　by　transmission　bandwidth　and　the　efficiency　of　the　sparse　matrix　multiplication　kernel.　In　this　paper,　we　optimize　the　layer　combination　parameters　of　LightGCN　by　Q-learning　and　add　hardware-friendly　activation　function　to　enhance　its　nonlinear　modeling　capability.　The　optimized　LightGCN　not　only　performs　well　on　the　original　dataset　and　some　molecular　prediction　tasks,　but　also　does　not　incur　significant　hardware　overhead.　Subsequently,　we　propose　an　efficient　architecture　to　accelerate　the　inference　of　LightGCN　to　improve　its　adaptability　to　real-time　tasks.　Comparing　S-LGCN　to　Intel(R)　Xeon(R)　Gold　5218R　CPU　and　NVIDIA　RTX3090　GPU,　we　observe　that　S-LGCN　is　1576.4　×　and　21.8　×　faster　with　energy　consumption　reductions　of　3211.6　×　and　71.6　×,　respectively.　Compared　to　FPGA-based　accelerator,　S-LGCN　demonstrates　1.5-4.5　×　lower　latency　and　2.03　×　higher　throughput.　©　2024　EDAA.