In multi-user and multi-task scenarios, using traditional decision algorithms to make computation offloading decisions for upcoming tasks in a short period can no longer meet users’ requirements for decision-making efficiency and resource utilization. Therefore, some studies have proposed deep reinforcement learning algorithms for offloading decisions to cater to various scenarios. However, most of these algorithms only consider the offloading first strategy, which leaves user equipment (UE) idle. This study improves the resource utilization of mobile edge computing (MEC) servers and UE and reduces the error rate of computation offloading. It proposes a decision offloading model combining local first and improved twin delayed deep deterministic policy gradient (TD3) algorithm and designs a simulation experiment. The experimental results show that the model can indeed improve the resource utilization of MEC servers and UE and reduce the error rate.