To　provide　a　better　support　for　various　vehicular　applications,　digital　twin　(DT),　as　an　emerging　technology,　can　enable　a　virtual　presentation　of　physical　vehicular　networks　to　reflect　the　current　network　state　through　real-time　data　updating.　However,　the　constrained　resources　and　high　data　updating　cost　may　degrade　the　performance　of　DT.　In　this　paper,　we　trade　off　the　data　updating　cost　and　the　performance　of　DT　to　adaptively　determine　the　resource　management　and　computation　offloading　in　vehicular　networks.　Specifically,　we　propose　a　novel　vehicle　to　vehicle　pairing　prediction　algorithm　assisted　by　DT　to　improve　the　offloading　decision　efficiency　and　investigate　the　effect　of　data　updating　frequency　on　prediction　accuracy.　Based　on　the　prediction　results,　we　formulate　a　joint　data　updating　frequency　selection,　offloading　decision　and　channel　allocation　problem　with　the　objective　of　minimizing　the　computation　and　communication　costs.　To　solve　the　formulated　problem,　we　propose　a　prediction-based　stability　maximum　pairing　algorithm　to　obtain　the　proper　task　offloading　strategy.　Moreover,　a　deep　Q-learning　network　algorithm　is　proposed　to　select　the　optimal　DT　data　updating　frequency　according　to　the　real-time　vehicular　network　state.　Based　on　the　obtained　optimal　solution,　we　further　propose　an　alternating　direction　method　of　multipliers-based　iteration　algorithm　to　optimize　the　computation　and　channel　resource　allocation　and　minimize　the　total　costs.　Numerical　results　are　provided　to　validate　the　effectiveness　and　efficiency　of　our　proposed　algorithms.