This study suggests an improved chaos sparrow search algorithm to overcome the problems of slow convergence speed and trapping in local optima in UAV 3D complex environment path planning. First, the quality of the initial solutions is improved by using a piecewise chaotic mapping during the population initialization phase. Secondly, a nonlinear dynamic weighting factor is introduced to optimize the update equation of producers, reducing the algorithm's reliance on producer positions and balancing its global and local exploration capabilities. In the meantime, an enhanced sine cosine algorithm optimizes the update equation of the scroungers to broaden the search space and prevent blind searches. Lastly, a dynamic boundary lens imaging reverse learning strategy is applied to prevent the algorithm from getting trapped in local optima. Experiments of UAV path planning on simple and complex maps are conducted. The results show that the proposed algorithm outperforms CSSA, SSA, and PSO algorithms with a respective time improvement of 22.4%, 28.8%, and 46.8% in complex environments and exhibits high convergence accuracy, which validates the proposed algorithm's usefulness and superiority.