Unmanned Aerial Vehicle （UAV） is flexible and easy to deploy， and can assist Mobile Edge Computing （MEC） to help wireless systems improve coverage and communication quality. However， there are challenges such as computational latency requirements and resource management in the research of UAV-assisted MEC systems. Aiming at the delay problem of UAV providing auxiliary calculation services to multiple ground terminals， a Twin Delayed Deep Deterministic policy gradient （TD3） based Task Offloading Algorithm for Delay Minimization （TD3-TOADM） was proposed. Firstly， the optimization problem was modeled as the problem of minimizing the maximum computational delay under energy constraints. Secondly， TD3-TOADM was used to jointly optimize terminal equipment scheduling， UAV trajectory and task offloading ratio to minimize the maximum computational delay. Simulation analysis results show that compared with the task offloading algorithms based on Actor-Critic （AC）， Deep Q-Network （DQN） and Deep Deterministic Policy Gradient （DDPG）， TD3-TOADM reduces the computational delay by more than 8.2%. It can be seen that TD3-TOADM algorithm has good convergence and robustness， and can obtain the optimal offloading strategy with low delay.