In　the　last　decade,　wire　+　arc　additive　manufacturing　(WAAM),　which　is　one　of　the　most　promising　metal　additive　manufacturing　technologies,　has　been　attracting　high　interest　from　both　academia　and　industry.　WAAM　systems　are　increasingly　employed　in　the　industry　and　academia,　but　there　are　still　several　challenges　and　barriers　to　process　stability　control.　The　process　stability　is　highly　dependent　on　how　the　molten　feed　wire　is　added　into　the　melt　pool,　which　is　known　as　the　droplet　transfer　mode.　To　ensure　a　stable　WAAM　deposition　process,　it　is　essential　to　maintain　the　transfer　mode　in　a　suitable　stable　status.　Without　an　effective　transfer　mode　control　method,　the　operators　need　to　determine　and　control　the　transfer　mode　based　on　their　experience　using　manual　adjustment,　which　is　difficult　to　achieve　in　a　long　period　of　production　process.　In　this　paper,　a　deep　learning-based　technology　was　proposed　for　the　control　of　the　droplet　transfer　mode　based　on　the　data　collected　from　the　WAAM　process.　A　long　short　term　memory　neural　network　was　applied　as　the　core　transfer　mode　classification　model.　A　time-series　data,　arc　voltage,　was　collected　and　statistical　and　frequency　features　were　extracted,　which　included　11　relevant　features,　as　the　inputs　of　the　classification　model.　Then,　the　distance　between　the　melted　wire　and　the　melt　pool　was　adjusted　based　on　the　determined　transfer　mode　to　keep　a　suitable　stability　of　the　process.　A　case　study　was　used　to　evaluate　the　proposed　approach　and　to　show　its　merit.　The　proposed　approach　was　compared　to　three　commonly　used　machine　learning　algorithms,　k-nearest　neighbours,　support　vector　machine,　and　decision　tree.　The　proposed　method　obtained　the　highest　accuracy　in　determining　the　transfer　mode,　which　was　over　91%.　The　performance　of　the　proposed　approach　was　also　evaluated　by　the　single-pass　and　oscillated　wall　building.　The　proposed　deep　learning　based　approach　improved　the　process　stability　in　real-time,　which　resulted　in　better　deposition　qualities,　in　terms　of　geometry　size　and　processing　cleanliness　compared　to　without　control.　Furthermore,　this　data-driven　method　could　be　applied　to　other　WAAM　processes　and　materials.