Directed against complex attack and defense confrontation between carrier aircraft and surface to-air missiles in modern warfare, this paper proposes an online decision-making method based on neural networks. In synthetic consideration of dual constraints of carrier aircraft maneuverability and target engagement, the aircraft's maneuvering strategy and the launch timing are optimized, improving the success rate of combat missions and meeting the needs of real-time decision-making. Firstly, dynamics models with anti-radiation missile, surface-to-air missile, and carrier aircraft are established, and a model of having attack and defense confrontation scenario, including all these three, is constructed. Through the simulation, the impact of different maneuvering strategies and launch timing on combat outcomes is analyzed, and the operation time is defined to measure success or failure of mission. And then, a genetic algorithm is employed to optimize the discrete-continuous hybrid parameter problem offline, obtaining the optimal carrier aircraft maneuvering strategy and anti-radiation missile launch timing, and taking such as these to construct a neural network training dataset and building a neural network model for training and validation. Finally, the effectiveness of neural network-based online decision-making is verified through simulation examples. The results show that this method can significantly expand the dominance zone of anti-radiation missiles, increase the mission success rate, and provide rapid predictions, meeting the needs of real-time decision-making.