Mobile edge computing (MEC) has gradually become an effective means of alleviating data overload. However, in highly crowded scenarios, edge servers fixed on base stations may fail to provide efficient services due to network overload. In view of the communication demands of low latency, a dual-layer unmanned aerial vehicle (UAV) with high mobility and easy deployment becomes an ideal choice for task offloading. The top UAV (T-UAV) equipped with computing resources can provide offloading services for the bottom UAV (B-UAV) capturing the on-site scene. The B-UAV equipped with a shooting device can choose to perform local computing or partially offload tasks to T-UAV for computation. In this study, a dual-layer UAV-assisted MEC system model is constructed, and a new offloading algorithm named deep deterministic policy gradient offloading algorithm based on composite prioritized experience replay (DDPG-CPER) is proposed. The algorithm comprehensively considers the continuity of decision variables and optimizes the task execution latency under constraints such as T-UAV resource scheduling and mobility, thus improving processing efficiency and response speed, so as to ensure a real-time viewing experience for on-site spectators. The simulation results show that the proposed algorithm exhibits faster convergence speed than baseline algorithms such as DDPG and can significantly reduce processing latency.