Gears　and　bearings　are　important　components　in　rotating　machinery　and　are　crucial　for　the　safety　and　operation　of　the　whole　mechanical　system.　Intelligent　fault　diagnosis　methods　based　on　deep-learning　algorithms　have　undergone　rapid　development　in　recent　years.　Despite　this,　integrating　fault　features　in　a　deep　network　construction　remains　a　challenge　for　intelligent　fault　diagnosis　of　rotating　machinery.　In　this　paper,　a　novel　impact　feature　extraction　deep　neural　network　(IFE-DN)　is　proposed　for　intelligent　gear　and　bearing　fault　diagnosis.　An　improved　three-layer　Laplace　wavelet　kernel　convolutional　neural　network　(LW-CNN),　where　the　Laplace　wavelet　kernel　is　designed　in　the　first　convolutional　layer,　is　constructed　to　extract　and　enhance　the　impact　features　in　the　vibration　signal.　Using　a　visualized　heat　map,　the　physical　meaning　of　the　LW-CNN＇s　extracted　features　is　explained　and　the　interpretability　of　the　network　model　is　enhanced.　The　wavelet　function　selection　in　the　deep　neural　network　is　also　discussed.　The　extracted　features　are　transferred　to　a　primary　capsule　layer　and　a　digital　capsule　layer.　With　a　feature　vector　converting　process　and　dynamic　routing　algorithm,　more　detailed　features　are　optimized　and　the　fault　types　are　classified.　Four　experimental　data　sets　from　different　laboratories　are　used　to　verify　the　performance　of　the　proposed　model,　and　t-distributed　stochastic　neighbour　embedding　is　carried　out　to　visually　analyze　the　extracted　features　in　different　layers.　The　results　of　the　analysis　of　gear　and　bearing　faults　of　different　types　and　defect　sizes　show　that　the　IFE-DN　presents　significant　accuracy　and　satisfactory　generalization　ability.