Focusing on the collaborative control of quadrotor Unmanned Aerial Vehicles (UAVs) and underactuated Unmanned Surface Vessels (USVs), this article presents a cooperative control algorithm based on fixed-time theory. By constructing an adaptive neural network to approximate model uncertainty and unknown disturbances in the system, this algorithm satisfies the coordinated motion constraints between UAVs and USVs, while achieving group control objectives such as trajectory tracking. Additionally, this article employs the fixed-time method in the Lyapunov function to analyze system stability, ensuring that the cooperative control error converges within a fixed time, ultimately enabling fast and stable tracking of targets between UAVs and USVs. Finally, the effectiveness of the proposed algorithm is verified through numerical simulation experiments.