In　the　fields　of　network　management　and　cyber　security,　encrypted　network　traffic　classification　is　a　critical　task.　Although　Deep　Learning　(DL)　models　have　been　used　in　this　field,　they　lack　explicit　control　over　data　feature　extraction,　resulting　in　the　retention　of　low-value　features,　which　confuses　the　training　and　negatively　impacts　the　classification　performance.　In　this　paper,　we　design　a　Contrastive　Learning　(CL)　based　encoder　for　extracting　robust　representation　vectors　with　valuable　features　from　unlabeled　data.　We　create　multiple　augmentation　samples　for　each　input　data　by　a　unique　augmenter.　By　narrowing　representation　vectors　among　similar　augmentation　samples　and　alienating　them　among　dissimilar　ones,　the　encoder　can　capture　valuable　features.　We　propose　CL-ETC,　a　semi-supervised　method　based　on　the　encoder.　In　CL-ETC,　a　well-trained　encoder　can　be　utilized　to　guide　supervised　classifier　training　to　increase　classification　performance　and　training　speed.　We　conduct　experiments　on　three　datasets,　and　the　findings　reveal　that　CL-ETC　outperforms　other　models　in　a　variety　of　metrics,　including　accuracy,　precision,　recall,　F1-score,　and　classifier　training　convergence　speed.