The　widespread　use　of　modern　network　communications　necessitates　effective　resource　control　and　management　in　TCP/IP　networks.　However,　most　existing　network　traffic　classification　methods　are　limited　to　labeled　known　classes　and　struggle　to　handle　open-set　scenarios,　where　known　classes　coexist　with　significant　volumes　of　unknown　classes　of　traffic.　To　solve　this　problem　more　accurately　and　reliably,　we　propose　RoNeTC.　This　method　achieves　high-precision　classification　by　enhancing　feature　extraction　and　quantifying　the　reliability　of　classification　decisions　through　uncertainty　estimation.　For　feature　extraction,　we　divide　each　packet　of　a　flow　into　three　views　for　parallel　training,　integrating　both　local　and　global　feature　representations　across　multiple　packets　to　enhance　accuracy.　We　devise　a　second-order　classification　probability　to　quantify　the　reliability　of　the　classifier＇s　results　and　to　visualize　the　reliability　of　open-set　flow　classification　in　terms　of　uncertainty.　Additionally,　we　dynamically　fuse　classification　decisions　from　multiple　views,　evaluating　decision　uncertainty　to　classify　known　and　unknown　flows　and　ensure　robust,　reliable　results.　We　compare　RoNeTC　with　four　state-of-the-art　(SOTA)　methods　in　six　open-set　scenarios.　RoNeTC　outperforms　the　other　methods　by　an　average　of　25.94%　in　F1　across　all　open-set　scenarios,　indicating　its　superior　performance　in　open-set　network　traffic　classification.