Aiming　at　the　problem　of　poor　robustness　of　the　intelligent　diagnostic　model,　a　fault　diagnosis　model　of　rolling　bearing　based　on　a　multi-dimension　input　convolutional　neural　network　(MDI-CNN)　is　proposed.　Compared　with　the　traditional　convolution　neural　network,　the　proposed　model　has　multiple　input　layers.　Therefore,　it　can　fuse　the　original　signal　and　processed　signal-making　full　use　of　advantages　of　the　convolutional　neural　networks　to　learn　the　original　signal　characteristics　automatically,　and　also　improving　recognition　accuracy　and　anti-jamming　ability.　The　feasibility　and　validity　of　the　proposed　MDI-CNN　are　verified,　and　its　advantages　are　proved　by　comparison　with　the　other　related　models.　Moreover,　the　robustness　of　the　model　is　tested　by　adding　the　noise　to　the　test　set.　Finally,　the　stability　of　the　model　is　verified　by　two　experiments.　The　experimental　results　show　that　the　proposed　model　improves　the　recognition　rate,　robustness　and　convergence　performance　of　the　traditional　convolution　model　and　has　good　generalization　ability.