Integrating　Knowledge　Graphs　(KGs)　into　recommendation　systems　as　supplementary　information　has　become　a　prevalent　strategy.　By　leveraging　the　semantic　relationships　between　entities　in　KGs,　recommendation　systems　can　better　comprehend　user　preferences.　Due　to　the　unique　structure　of　KGs,　methods　based　on　Graph　Neural　Networks　(GNNs)　have　emerged　as　the　current　technical　trend.　However,　existing　GNN-based　methods　struggle　to　(1)　filter　out　noisy　information　in　real-world　KGs,　and　(2)　differentiate　the　item　representations　obtained　from　the　knowledge　graph　and　bipartite　graph.　In　this　paper,　we　introduce　a　novel　model　called　Attention-enhanced　and　Knowledge-fused　Dual　item　representations　Network　for　recommendation　(namely　AKDN)　that　employs　attention　and　gated　mechanisms　to　guide　aggregation　on　both　knowledge　graphs　and　bipartite　graphs.　In　particular,　we　firstly　design　an　attention　mechanism　to　determine　the　weight　of　each　edge　in　the　information　aggregation　on　KGs,　which　reduces　the　influence　of　noisy　information　on　the　items　and　enables　us　to　obtain　more　accurate　and　robust　representations　of　the　items.　Furthermore,　we　exploit　a　gated　aggregation　mechanism　to　differentiate　collaborative　signals　and　knowledge　information,　and　leverage　dual　item　representations　to　fuse　them　together　for　better　capturing　user　behavior　patterns.　We　conduct　extensive　experiments　on　two　public　datasets　which　demonstrate　the　superior　performance　of　our　AKDN　over　state-of-the-art　methods,　like　Knowledge　Graph　Attention　Network　(KGAT)　and　Knowledge　Graph-　based　Intent　Network　(KGIN).