Research on optimization decision of command control based on deep learning

In the context of the deepening trend of informationization and intellectualization in modern warfare, traditional command control systems are facing many challenges in processing complex battlefield information and improving decision-making efficiency, while deep learning technology, with its powerful data processing and pattern recognition capabilities, provides a new solution for command control optimization. This paper first outlines the theory of deep learning and command control, focuses on the structure and learning mechanism of BP neural network, and analyzes the application of deep learning in command control combined with the characteristics of C4I system. Secondly, the design scheme of command control optimization decision-making system based on deep learning is discussed in detail, including its neural network topology, learning process and testing method. Finally, the validity of the model is verified by experiments, and the results of optimization decision in different battlefield environments are analyzed. The research shows that deep learning technology can improve the intelligent level of command control system, and provide strong support for rapid and accurate decision-making in complex combat environment.