The model predictive control(MPC) method has been applied in the path planning for unmanned aerial vehicle(UAV) swarm. However, it has some disadvantages, such as high computation consumption, long time single-step execution, et al. These disadvantages make the MPC method difficult for real-time implementation which requires high control updating frequency. The offline MPC method requires accurate map information and struggles with handling unpredictable dynamic obstacles. In this paper, a path planning strategy is proposed which combines offline MPC for the global planning with the online improved artificial potential field(APF) for the UAVs' local planning. This approach enhances the UAV's obstacle avoidance capability while ensuring safe and smooth trajectories generated by the MPC. This paper introduces an adjustment force to solve the local minimum problem in the traditional APF method. A repulsive function based on the relative distance between the target and UAVs, and an attractive function are designed to alleviate the UAVs' low speed problem near the target point. An event-triggered UAV trajectory modification and recovery strategy is also designed, enabling the UAV to perform dynamic obstacle avoidance behaviors only when it is necessary, thus maximizing the utilization of the original planned trajectory. Simulation results demonstrate that the proposed method can make the UAVs reach the target point with excellent dynamic obstacle avoidance capabilities.