Lung　cancer　is　the　most　threatening　tumor　disease　to　human　health.　Early　detection　is　crucial　to　improve　the　survival　rate　and　recovery　rate　of　lung　cancer　patients.　Existing　methods　use　the　two-dimensional　multi-view　framework　to　learn　lung　nodules　features　and　simply　integrate　multi-view　features　to　achieve　the　classification　of　benign　and　malignant　lung　nodules.　However,　these　methods　suffer　from　the　problems　of　not　capturing　the　spatial　features　effectively　and　ignoring　the　variability　of　multi-views.　Therefore,　this　paper　proposes　a　three-dimensional　(3D)　multi-view　convolutional　neural　network　(MVCNN)　framework.　To　further　solve　the　problem　of　different　views　in　the　multi-view　model,　a　3D　multi-view　squeeze-and-excitation　convolution　neural　network　(MVSECNN)　model　is　constructed　by　introducing　the　squeeze-and-excitation　(SE)　module　in　the　feature　fusion　stage.　Finally,　statistical　methods　are　used　to　analyze　model　predictions　and　doctor　annotations.　In　the　independent　test　set,　the　classification　accuracy　and　sensitivity　of　the　model　were　96.04%　and　98.59%　respectively,　which　were　higher　than　other　state-of-the-art　methods.　The　consistency　score　between　the　predictions　of　the　model　and　the　pathological　diagnosis　results　was　0.948,　which　is　significantly　higher　than　that　between　the　doctor　annotations　and　the　pathological　diagnosis　results.　The　methods　presented　in　this　paper　can　effectively　learn　the　spatial　heterogeneity　of　lung　nodules　and　solve　the　problem　of　multi-view　differences.　At　the　same　time,　the　classification　of　benign　and　malignant　lung　nodules　can　be　achieved,　which　is　of　great　significance　for　assisting　doctors　in　clinical　diagnosis.　Copyright　©2022　Journal　of　Biomedical　Engineering.　All　rights　reserved.