Optimization of Integrated Passenger and Cargo Cabins in Civil Aviation Through AI-Driven Design

This study proposes an AI-based optimization approach for civil aviation passenger and cargo cabin design, focusing on maximizing space utilization, structural integrity, comfort, and energy efficiency.By combining deep learning for spatial feature extraction and reinforcement learning for dynamic layout optimization, the proposed method efficiently generates cabin configurations that meet multiple design objectives. Experimental results demonstrate significant improvements over traditional design methods, with space utilization increasing by 16.2%, structural integrity enhanced by 17.3%, and energy consumption reduced by $\mathbf{2 2. 7 \%}$. This approach offers a novel, data-driven solution for optimizing cabin layouts, with potential applications in both commercial and freight aviation.