Computer-aided　diagnosis　based　on　computed　tomography　(CT)　image　can　realize　the　detection　and　classification　of　pulmonary　nodules,　and　improve　the　survival　rate　of　early　lung　cancer,　which　has　important　clinical　significance.　In　recent　years,　with　the　rapid　development　of　medical　big　data　and　artificial　intelligence　technology,　the　auxiliary　diagnosis　of　lung　cancer　based　on　deep　learning　has　gradually　become　one　of　the　most　active　research　directions　in　this　field.　In　order　to　promote　the　deep　learning　in　the　detection　and　classification　of　pulmonary　nodules,　we　reviewed　the　research　progress　in　this　field　based　on　the　relevant　literatures　published　at　domestic　and　overseas　in　recent　years.　This　paper　begins　with　a　brief　introduction　of　two　widely　used　lung　CT　image　databases:　lung　image　database　consortium　and　image　database　resource　initiative　(LIDC-IDRI)　and　Data　Science　Bowl　2017.　Then,　the　detection　and　classification　of　pulmonary　nodules　based　on　different　network　structures　are　introduced　in　detail.　Finally,　some　problems　of　deep　learning　in　lung　CT　image　nodule　detection　and　classification　are　discussed　and　conclusions　are　given.　The　development　prospect　is　also　forecasted,　which　provides　reference　for　future　application　research　in　this　field.　Copyright　©　2019　by　Editorial　Office　of　Journal　of　Biomedical　Engineering.