In　recent　years,　to　address　the　classification　problem　of　unknown　traffic　in　open　network　environments,　numerous　deep　learning　(DL)-based　classification　techniques　have　been　proposed.　Among　these　techniques,　the　single　threshold-based　DL　method　has　gained　significant　popularity.　However,　the　classification　boundary　constructed　by　this　method　is　inaccurate,　thus　leading　to　the　misclassification　of　known　and　unknown　traffic.　In　this　paper,　we　propose　an　adaptive　classification　and　updating　method　for　accurate　application-level　classification　of　known　and　unknown　traffic　in　open　environments.　We　adaptively　construct　individualized　and　accurate　decision　boundaries　for　each　class　of　known　traffic,　which　avoids　misclassification　cases　caused　by　a　single　threshold　and　achieves　accurate　separation　of　known　and　unknown　traffic.　To　provide　a　more　accurate　feature　representation　for　the　construction　of　the　decision　boundary,　we　also　introduce　an　additional　angular　loss　function　to　guide　the　model　in　learning　more　accurate　flow　features.　In　addition,　to　cope　with　the　constant　emergence　of　new　classes　of　traffic　in　real　network　environments,　our　proposed　method　adaptively　updates　the　model　to　respond　to　changes　in　network　traffic　classes.　The　proposed　method　is　evaluated　on　two　datasets,　and　the　experimental　results　show　that　our　proposed　method　has　excellent　classification　accuracy　and　outperforms　the　state-of-the-art　unknown　traffic　classification　methods.