Traffic　Engineering　(TE)　promotes　the　performance　of　hybrid　Software　Defined　Networks　(hybrid　SDN)　through　optimizing　traffic　route　selection.　To　handle　dynamic　network　traffic,　existing　machine　learning-based　TE　methods　in　hybrid　SDNs　focus　on　leveraging　Reinforcement　Learning　(RL)　to　learn　the　mapping　between　the　dynamic　traffic　demands　and　the　traffic　splitting　ratios.　However,　with　the　huge　network　state　space　incurred　by　the　dynamic　network　traffic　and　increasing　network　scale,　it　is　hard　for　the　RL-agent　to　learn　and　converge　to　the　optimal　mapping　between　traffic　demands　and　traffic　splitting　ratios,　thus　the　network　performance　suffers　a　degradation　in　dynamic　network　environment.　To　tackle　this　issue,　we　innovatively　propose　a　TE　approach　that　combines　Contrastive　learning　(CL)　and　RL.　Specifically,　to　reduce　huge　state　space,　we　design　to　learn　the　mapping　between　network　traffic　features　and　routing　policy　rather　than　learning　the　mapping　between　traffic　demand　and　routing　policy.　To　well　capture　the　features　of　traffic　demands,　we　leverage　CL　to　train　a　feature　encoder　for　representing　network　traffic.　We　conduct　extensive　experiments　on　real　network　topologies　datasets　and　the　experimental　results　demonstrate　that　our　proposed　algorithm　provides　significant　network　performance　improvements　over　state-of-arts.　©　2025　Elsevier　Ltd