To address the issues of poor search accuracy, slow convergence speed and easy fallback to local optima in solving unmanned aerial vehicle (UAV) path coordination planning problems using the hyperbolic sine-cosine optimization （SCHO） algorithm, a segment-guided and dynamical partitioned improved hyperbolic sine-cosine optimization （SDSCHO） algorithm is proposed. A three-dimensional geographic model of UAV flight and threat conditions is established, and a path coordination planning cost model is constructed that integrates path length, obstacle threat, flight altitude and turning angle. And SCHO algorithm is comprehensively improved by introducing chaos Circle mapping for population initialization, nonlinear oscillation conversion factor, segment-guided and reverse escape optimization, and dynamic boundary partitioning-assisted position update strategy. The improved algorithm SDSCHO is used to solve the UAV path coordination planning problem. On multiple benchmark functions with different characteristics, the optimizing tests are carried out with seven similar algorithms. The results prove that SDSCHO performs better in optimization accuracy and convergence performance. Finally, by building a three-dimensional mountain model with different obstacles, SDSCHO is applied to solve UAV single-path and multi-path coordination planning scenarios, which can further confirm the superiority of our algorithm in handling actual optimization problems.