Graph　convolutional　networks　have　achieved　remarkable　success　in　the　field　of　multi-view　learning.　Unfortunately,　most　graph　convolutional　network-based　multi-view　learning　methods　fail　to　capture　long-range　dependencies　due　to　the　over-smoothing　problem.　Many　studies　have　attempted　to　mitigate　this　issue　by　decoupling　graph　convolution　operations.　However,　these　decoupled　architectures　lead　to　the　absence　of　feature　transformation　module,　thus　limiting　the　expressive　power　of　the　model.　To　this　end,　we　propose　an　information-controlled　graph　convolutional　network　for　multi-view　semi-supervised　classification.　In　the　proposed　method,　we　maintain　the　paradigm　of　node　embeddings　during　propagation　by　imposing　orthogonality　constraints　on　the　feature　transformation　module.　By　further　introducing　a　damping　factor　based　on　residual　connections,　we　theoretically　demonstrate　that　the　proposed　method　can　alleviate　the　over-smoothing　problem　while　retaining　the　feature　transformation　module.　Furthermore,　we　prove　that　the　proposed　model　can　stabilize　both　forward　inference　and　backward　propagation　in　graph　convolutional　networks.　Extensive　experimental　results　on　benchmark　datasets　demonstrate　the　effectiveness　of　the　proposed　method.