Aiming　at　the　problem　that　rolling　bearings　signal　is　susceptible　to　noise　interference　and　the　poor　robustness　of　an　intelligent　diagnosis　model,　a　fault　diagnosis　model　for　rolling　bearings　based　on　a　multi-input　layer　convolutional　neural　network　was　proposed　on　the　basis　of　a　one-dimensional　convolutional　network.　Compared　with　the　traditional　convolutional　neural　network　diagnosis　model,　the　model　had　multiple　input　layers.　The　data　of　initial　input　layer　was　the　original　signal,　in　order　to　maximize　the　advantages　of　the　convolutional　network　to　automatically　learn　the　original　signal　characteristics.　The　spectral　analysis　data　could　be　input　into　the　network　at　any　position　of　the　model,　in　order　to　improve　the　recognition　accuracy　and　anti-jamming　ability　of　the　model.　Firstly,　through　a　simulation　test　of　rolling　bearing,　the　feasibility　and　validity　of　the　proposed　method　were　verified.　Then,　the　robustness　of　the　model　was　tested　by　adding　noise　to　the　test　set,　and　the　recognition　performance　of　the　model　in　strong　noise　environment　was　improved　by　using　an　incremental　learning　method.　Finally,　through　the　example　of　rolling　bearing　fault,　the　recognition　performance　and　generalization　ability　of　the　model　were　verified.　Experimental　results　show　that　the　proposed　model　can　improve　the　recognition　rate　and　convergence　performance　of　the　traditional　convolutional　model,　and　has　good　robustness　and　generalization　ability.　©　2020,　Editorial　Office　of　Journal　of　Vibration　and　Shock.　All　right　reserved.