Aiming　at　the　problem　of　real-time　monitoring　of　the　tool　in　the　working　state　when　the　machine　tool　is　producing　the　same　part　in　batches,　a　tool　wear　state　monitoring　method　based　on　the　spindle　power　signal　and　the　machining　parameters　of　the　machine　tool　is　proposed.　First,　the　linear　model　of　deep　learning　is　used　to　fit　the　linear　model　between　machining　parameters,　tool　wear　and　spindle　power　by　using　historical　data,　and　then　by　setting　the　wear　amount　of　the　tool,　the　threshold　value　of　the　spindle　power　under　different　wear　conditions　is　obtained,　and　the　wear　amount　of　the　tool　is　monitored　by　judging　the　relationship　between　the　spindle　power　and　the　threshold　value.　High　availability　compared　to　traditional　downtime　measurement　methods.　Finally,　through　the　experiment,　Collect　the　spindle　power　signal　of　the　vertical　machining　centre　to　predict　the　wear　state　of　the　tool,　and　compare　it　with　the　actual　wear　amount.　The　results　show　that　the　proposed　method　has　high　accuracy　and　applicability,　it　can　realize　long-term　online　monitoring　of　tool　wear　under　normal　production　conditions.　©　2022　IEEE.