To address the unmanned aerial vehicle (UAV) path planning problem in complex mountainous terrain and obstacle-threatened areas, a dynamic UAV path planning method based on an improved Black Kite optimization algorithm is proposed, aiming to enhance the path planning performance and safety of UAVs in dynamic complex environments. Firstly, a dynamic mountainous environment model is established by designing mountainous terrain, obstacles, dynamic threat areas, and dynamic targets. Secondly, an adaptive attack strategy is proposed to accelerate the convergence speed in the early stages of the algorithm and balance the global search and local exploitation capabilities. A linear lock optimization strategy is designed to obtain high-quality individuals and accelerate population convergence. Finally, by designing a variable scaling factor to improve the differential evolution strategy and integrating it into the Black Kite algorithm, the ability to avoid local optima is enhanced, and a dynamic response mechanism is proposed to cope with environmental changes. To validate the performance of the proposed improved algorithm, it is compared with existing intelligent algorithms using the CEC2022 test functions and environmental models of different scales. The results show that the convergence accuracy of the proposed algorithm is improved by 6.25% and the standard deviation is reduced by 54.6% compared to the standard Black Kite algorithm. The experimental results demonstrate that the improved Black Kite optimization algorithm has significant advantages in terms of convergence speed and accuracy, effectively handling dynamic UAV path planning problems and improving the path planning performance of UAVs in complex environments.