Sustainable Optimization Design of Architectural Space Based on Visual Perception and Multi-Objective Decision Making

This study proposes an integrated computational framework that combines deep learning-based visual perception analysis with multi-criteria decision making to optimize indoor architectural layouts in terms of both visual coherence and sustainability. The framework initially employs a deep learning method leveraging edge pixel feature recombination to extract critical spatial layout features and determine key visual focal points. A fusion model is then constructed to preprocess visual representations of interior layouts. Subsequently, an evolutionary deep learning algorithm is adopted to optimize parameter convergence and enhance feature extraction accuracy. To support comprehensive evaluation and decision making, an improved Analytic Hierarchy Process (AHP) is integrated with the entropy weight method, enabling the fusion of objective, data-driven weights with subjective expert judgments. This dual-focus framework addresses two pressing challenges in architectural optimization: sensitivity to building-specific spatial features and the traditional disconnect between perceptual analysis and sustainability metrics. Experimental results on a dataset of 25,400 building images demonstrate that the proposed method achieves a feature detection accuracy of 92.3%, surpassing CNN (73.6%), RNN (68.2%), and LSTM (75.1%) baselines, while reducing the processing time to under 0.95 s and lowering the carbon footprint to 17.8% of conventional methods. These findings underscore the effectiveness and practicality of the proposed model in facilitating intelligent, sustainable architectural design.