This paper proposed an Event-triggered Curriculum DDPG algorithm to improve the efficiency and accuracy of dynamic target tracking for UAVs. The algorithm combined Deep Deterministic Policy Gradient （DDPG） and YOLO object detection technology. It introduced an event-triggered mechanism to dynamically adjust the policy update frequency, enhancing decision-making efficiency. Additionally, it incorporated curriculum learning to create a staged training framework, gradually improving the UAV′s adaptability to complex tasks. Experimental results showed that the ETC-DDPG algorithm effectively improved the tracking efficiency of dynamic target tracking task and the stability of training process, and reduced the demand for computing resources, achieving a success rate of 93.357%. Compared with the original-DDPG algorithm and ETC-TD3 algorithm, the success rate is improved by 56.175% and 37.1% respectively. The collaborative effect of the event-triggered mechanism and curriculum learning was verified by ablation experiment, providing a reference for autonomous task execution in UAVs.