In　the　industrial　e-commerce　recommender　systems,　the　sparsity　of　user-item　interaction　limits　the　improvement　of　the　performance　of　collaborative　filtering　recommendation.　Some　studies　have　leveraged　attribute　co-occurrence　or　similar　neighbors　to　enhance　the　semantic　representation　quality　of　users　and　items.　Previous　methods　consider　collaborative　signals　of　homogeneous　type　nodes,　such　as　＜　user,　user　＞＞　user　and　＜　item,　item　＞＞　item.　By　exploiting　homogeneous　and　heterogeneous　signals　of　attribute　and　neighbor　views,　we　design　a　multiview　graph　collaborative　filtering　(MVGCF)　network　for　recommendation.　The　MVGCF　model　utilizes　both　co-occurrence　features　of　various　attribute　values　and　collaborative　preference　of　various　neighbors　to　learn　the　embedding　representation　of　nodes.　Experimental　results　show　that　the　MVGCF　is　superior　to　the　state-of-the-art　models　in　AUC　and　logloss　metrics　by　1.41%　and　3.12%　for　MovieLens　1M　dataset,　and　by　2.35%　and　2.31%　for　BookCrossing　dataset.　Aiming　at　the　sparse　problem　with　a　small　amount　of　interaction　records,　our　findings　is　that　attribute　co-occurrence　and　neighbor　collaboration　can　improve　the　accuracy　and　provide　a　good　explanation　for　e-commerce　recommender　systems.