In order to solve the problems of high cost， poor mobility and difficulty in coping with emergency in large-scale deployment of fixed edge computing nodes， a computing task offloading algorithm based on deep reinforcement learning is proposed to meet the needs of computing-intensive and delay-sensitive mobile services. Considering constraints such as the flight range， flight speed and system fairness benefits of multiple unmanned aerial vehicles （UAVs）， the method aims to minimize the weighted sum of the average computing delay of the network and the UAV energy consumption. This non-convex and non-deterministic polynomial（NP）-hard problem is transformed into a partially observed Markov decision process， and a multi-agent deep deterministic policy gradient algorithm is used for mobile user offloading decision and UAV flight trajectory optimization. Simulation results show that the proposed algorithm outperforms the baseline algorithm in terms of fairness of mobile service terminals， average system delay and total energy consumption of multiple UAVs. Especially， the proposed algorithm can obtain the optimal power consumption control under different computing performance. When the CPU frequency is 12.5 GHz， the energy consumption is 29.16% lower than the Cruise algorithm， and 8.67% lower than the advantage actor-critic（A2C） algorithm.