A　wealth　of　semantic　features　exist　in　the　reviews　written　by　users,　such　as　rich　information　on　item　features　and　implicit　preferences　of　users.　Existing　review-based　recommendation　models　usually　employ　Convolutional　Neural　Networks　(CNNs)　to　learn　representations　of　users　and　items　from　reviews.　However,　these　CNNs-based　models　suffer　from　two　main　problems:　(1)　they　only　consider　the　information　of　the　word　itself　during　the　convolution,　ignoring　the　high-order　contextual　semantic　information　of　the　word;　(2)　they　model　user/item　attributes　in　a　static　and　independent　way,　ignoring　the　potential　feature　interaction　between　them.　Therefore,　we　propose　a　novel　Review-aware　Interactive　Graph　Convolutional　Network　(RI-GCN)　for　review-based　item　recommendation.　Specifically,　we　design　a　Review-aware　GCN　component　to　model　the　message　propagation　of　graphs　constructed　from　reviews,　capturing　the　contextual　features　of　words.　A　feature　interactive　GCN　component　is　then　proposed　to　capture　the　user/item　high-order　collaborative　features　in　the　user-item　graph,　enabling　the　model　to　further　complement　and　refine　u　ser/item　a　ttributes.　Finally,　we　adopt　a　Factorization　Machine　model　for　the　recommendation　task.　Experimental　results　demonstrate　that　the　proposed　model　is　superior　to　state-of-the-art　models.　©　2022　IEEE.