A new A* unmanned aerial vehicle (UAV) path planning algorithm based on optimization heuristic function and B-spline curve is proposed to address the problems of excessive search nodes, complex paths, and low efficiency in the traditional A* algorithm for UAV path planning. By constructing a novel A* algorithm heuristic function that includes logarithmic factors of obstacles and dynamic weight coefficients, the search nodes for global path planning have been effectively reduced. The traditional 8 search directions are reduced to 5 based on balancing the UAV flight"s dynamic performance and computational efficiency, thereby reducing the algorithm"s computational complexity. In addition, the improved Floyd algorithm removes redundant intermediate nodes in the path, reducing unnecessary turns in the trajectory. Ultimately, the cubic B-spline interpolation algorithm is employed to refine the planned path, ensuring a smoother flight trajectory for the UAV. By comparing and analyzing the improved A* algorithm, traditional A* algorithm, and ant colony algorithm in two different map environments, the experimental results demonstrate that the path generated by the improved A* algorithm is more efficient and smoother.