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For the fixed wing UAV path planning with high real-time requirements, time cost is introduced into the optimization model to fit the actual project, and the new improved whale optimization algorithm with early warning mechanism is proposed to complete the optimization solution. The algorithm establishes individual early warning probability by ranking fitness of the population, and uses it to control the selection of update mechanism. By introducing the weight coefficient associated with the early warning probability to control the expansion and contraction of the spiral update mechanism, and using Lévy flight to improve the random walk mechanism to accelerate the convergence, the goal of balancing the development and exploration capabilities of each mechanism is achieved, which is beneficial to alleviate the problems of whale optimization algorithm, such as slow convergence speed and low convergence accuracy. The simulation experiment uses benchmark function to prove the effectiveness of the algorithm, and the simulation of path planning in different dimensions and distances shows the superiority of the improved algorithm. The simulation results show that the algorithm accuracy can be improved by 8.0% when dealing with low dimension path planning and the convergence speed of algorithm can be improved by 50%.