Deep Learning-Based Optimization Model for Real Estate Spatial Layout

Traditional real estate spatial layout optimization methods often rely on expert experience or rule-based optimization algorithms, and it is difficult to fully tap potential patterns and global optimal solutions in complex urban environments. To address this, this paper proposes a deep learning-based real estate spatial layout optimization model, GNNPPO, which combines graph neural networks with reinforcement learning, demonstrating significant innovation in spatial data understanding and optimization decision-making. Firstly, this paper constructs a GNN-based spatial feature extraction module, which learns geographic spatial relationships and functional block distributions to extract high-dimensional spatial representations, enhancing the expressive power of layout features. Secondly, a reinforcement learning framework is introduced, modeling spatial layout optimization as a dynamic decision-making process, where the reinforcement learning agent simulates and deduces based on historical data, autonomously learning the optimal layout strategy. Compared to traditional optimization algorithms, this model can effectively adapt to complex spatial constraints, improving the rationality of site selection and land use efficiency in real estate projects. Experimental results show that this method outperforms traditional methods in real estate spatial optimization tasks, better balancing land value maximization with residential comfort, providing more scientific decision support for smart city planning and real estate development.