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.