In　the　process　of　milling,　tool　wear　directly　affects　the　quality　and　accuracy　of　workpieces.　Online　recognition　of　milling　cutter　wear　state　has　been　and　remains　a　growing　interest　in　intelligent　manufacturing　to　increase　the　machining　efficiency　and　control　the　unqualified　rate　of　workpieces.　The　effective　value　of　spindle　current　can　effectively　characterize　the　wear　state　of　milling　cutter,　but　it　will　change　along　with　machining　process　parameters,　which　are　not　suitable　for　the　wear　state　recognition　of　milling　cutter　(WSRMC)　under　complex　working　conditions.　We　present　LeNet-WSRMC　network,　a　novel　approach　to　recognize　wear　state　of　milling　cutter　based　on　the　clutter　signal　of　spindle　current.　The　cutting　vibration　and　tool　wear　are　the　main　reasons　for　exciting　the　dynamic　cutting　force　and　the　clutter　signal　of　spindle　current.　In　order　to　fully　describe　the　generation　mechanism　of　the　clutter　signal,　we　divide　the　wear　state　of　milling　cutter　into　four　categories　(i.e.,　normal　wear,　severe　wear,　abnormal　vibration　caused　by　tool　wear,　and　abnormal　vibration　caused　by　improper　selection　of　cutting　parameter　when　the　tool　is　sharp).　LeNet-WSRMC　network　uses　the　deep　convolutional　neural　network　(DCNN)　model　to　extract　features　from　the　spindle　current　clutter　signal　(SCCS)　as　the　wear　state　of　milling　cutter　classification　index.　A　series　of　experiments　with　different　cutting　parameters　and　conditions　are　implemented　to　validate　the　effectiveness　and　generalization　of　our　proposed　methodology.　The　experimental　results　show　that　this　method　can　realize　the　online　accurate　recognition　of　the　wear　state　of　milling　cutter　under　the　condition　of　complex　working　condition.　This　study　lays　a　foundation　for　the　prediction　of　the　remaining　life　of　the　milling　cutter　under　complex　working　conditions　and　the　reasonable　formulation　of　the　replacement　rules　of　the　milling　cutter.