In　practical　applications,　multi-view　data　depicting　objects　from　assorted　perspectives　can　facilitate　the　accuracy　increase　of　learning　algorithms.　However,　given　multi-view　data,　there　is　limited　work　for　learning　discriminative　node　relationships　and　graph　information　simultaneously　via　graph　convolutional　network　that　has　drawn　the　attention　from　considerable　researchers　in　recent　years.　Most　of　existing　methods　only　consider　the　weighted　sum　of　adjacency　matrices,　yet　a　joint　neural　network　of　both　feature　and　graph　fusion　is　still　under-explored.　To　cope　with　these　issues,　this　paper　proposes　a　joint　deep　learning　framework　called　Learnable　Graph　Convolutional　Network　and　Feature　Fusion　(LGCN-FF),　consisting　of　two　modules:　feature　fusion　network　and　learnable　graph　convolutional　network.　The　former　aims　to　learn　an　underlying　feature　representation　from　heterogeneous　views,　while　the　latter　explores　a　more　discriminative　graph　fusion　via　learnable　weights　and　a　parametric　activation　function　dubbed　Differentiable　Shrinkage　Activation　(DSA)　function.　The　proposed　LGCN-FF　is　validated　to　be　superior　to　various　state-of-the-art　methods　in　multi-view　semi-supervised　classification.