Intelligent　vehicle　applications　provide　convenience　but　raise　privacy　and　security　concerns.　Misuse　of　sensitive　data,　including　vehicle　location,　and　facial　recognition　information,　poses　a　threat　to　user　privacy.　Hence,　traffic　classification　is　vital　for　promptly　overseeing　and　controlling　applications　with　sensitive　information.　In　this　paper,　we　propose　ET-　Net,　a　framework　that　combines　multiple　features　and　leverages　self-attention　mechanisms　to　learn　deep　relationships　between　packets.　ET-Net　employs　a　multi-　similarity　triplet　network　to　extract　features　from　raw　bytes,　and　exploits　self-attention　to　capture　long-range　dependencies　within　packets　in　a　session　and　contextual　information　features.　Additionally,　we　utilizing　the　loss　function　to　more　effectively　integrate　information　acquired　from　both　byte　sequences　and　their　corresponding　lengths.　Through　simulated　evaluations　on　datasets　with　similar　attributes,　ET-Net　demonstrates　the　ability　to　finely　distinguish　between　nine　categories　of　applications,　achieving　superior　results　compared　to　existing　methods.