To enhance the firing rate of a guided double-barrel rotary-bore gun, we propose a mathematical model grounded in gas dynamics theory, which integrates the internal ballistic process with the guidance system of the gas device. Initially, numerical simulations using this computational model are conducted to derive the motion characteristic curve of the weapon system automaton, and the model's accuracy is validated by comparing its predictions with experimental results. Building on this, we systematically adjust parameters of the air guide device—including the diameter and opening position of the air guide hole, piston diameter, rotational inertia of the rotating chamber, and mass of the slide plate—to investigate how structural changes affect shooting performance and identify the primary influencing factors. Finally, a BP neural network is employed to develop an agent model linking these factors to firing speed, and a particle swarm algorithm is utilized to determine the optimal structural parameter combinations for maximum firing speed. The optimization results indicate that the enhanced firing rate reaches 2350 rounds per minute, representing a 17.5% improvement over the original rate, thereby significantly enhancing shooting performance.