Ecological　problems　and　pollution　problems　must　be　faced　and　solved　in　the　sustainable　development　of　a　country.　With　the　continuous　development　of　image　analysis　technology,　it　is　a　good　choice　to　use　machine　to　automatically　judge　the　external　environment.　In　order　to　solve　the　problem　of　smoke　extraction　and　exhaust　monitoring,　we　need　the　applicable　database.　Considering　the　number　of　databases　that　can　be　used　to　detect　smoke　is　small　and　these　databases　have　fewer　types　of　pictures,　we　subdivide　the　smoke　detection　database　and　get　a　new　database　for　smoke　and　smoke　color　detection.　The　main　purpose　is　to　preliminarily　identify　pollutants　in　smoke　and　further　develop　smoke　image　detection　technology.　We　discuss　eight　kinds　of　convolutional　neural　network,　they　can　be　used　to　classify　smoke　images.　Testing　different　convolutional　neural　networks　on　this　database,　the　accuracy　of　several　existing　networks　is　analyzed　and　compared,　and　the　reliability　of　the　database　is　also　verified.　Finally,　the　possible　development　direction　of　smoke　detection　is　summarized.　©　2020,　Springer　Nature　Singapore　Pte　Ltd.