PurposeEarly　screening　is　crucial　to　improve　the　survival　rate　and　recovery　rate　of　lung　cancer　patients.　Computer-aided　diagnosis　system　(CAD)　is　a　powerful　tool　to　assist　clinicians　in　early　diagnosis.　Lung　nodules　are　characterized　by　spatial　heterogeneity.　However,　many　attempts　use　the　two-dimensional　multi-view　(MV)　framework　to　learn　and　simply　integrate　multiple　view　features.　These　methods　suffer　from　the　problems　of　not　capturing　the　spatial　characteristics　effectively　and　ignoring　the　variability　of　multiple　views.　In　this　paper,　we　propose　a　three-dimensional　MV　convolutional　neural　network　(3D　MVCNN)　framework　and　embed　the　squeeze-and-excitation　(SE)　module　in　it　to　further　address　the　variability　of　each　view　in　the　MV　framework.　MethodsFirst,　the　3D　multiple　view　samples　of　lung　nodules　are　extracted　by　the　spatial　sampling　method,　and　a　3D　CNN　is　established　to　extract　3D　abstract　features.　Second,　build　a　3D　MVCNN　framework　according　to　the　3D　multiple　view　samples　and　3D　CNN.　This　framework　can　learn　more　features　of　different　views　of　lung　nodules,　taking　into　account　the　characteristics　of　spatial　heterogeneity　of　lung　nodules.　Finally,　to　further　address　the　variability　of　each　view　in　the　MV　framework,　a　3D　MVSECNN　model　is　constructed　by　introducing　a　SE　module　in　the　feature　fusion　stage.　For　training　and　testing　purposes　we　used　independent　subsets　of　the　public　LIDC-IDRI　dataset.　ResultsFor　the　LIDC-IDRI　dataset,　this　study　achieved　96.04%　accuracy　and　98.59%　sensitivity　in　the　binary　classification,　and　87.76%　accuracy　in　the　ternary　classification,　which　was　higher　than　other　state-of-the-art　studies.　The　consistency　score　of　0.948　between　the　model　predictions　and　pathological　diagnosis　was　significantly　higher　than　that　between　the　clinician＇s　annotations　and　pathological　diagnosis.　ConclusionsThe　results　show　that　our　proposed　method　can　effectively　learn　the　spatial　heterogeneity　of　nodules　and　solve　the　problem　of　multiple　view　variability.　Moreover,　the　consistency　analysis　indicates　that　our　method　can　provide　clinicians　with　more　accurate　results　of　benign-malignant　lung　nodule　classification　for　auxiliary　diagnosis,　which　is　important　for　assisting　clinicians　in　clinical　diagnosis.