In the rapidly evolving field of generative design, artificial intelligence has demonstrated remarkable capabilities in optimizing geometric forms and environmental performance. However, there remains a systematic gap in encoding the structured dimensions of human spatial cognition. To address this deficit, we propose SEEP (Spatial Experience Encoding Parameters), a framework that quantifies abstract spatial experiences into computable six-dimensional vector sequences: Openness (O), Sequential Rhythm (R), Landmark Visibility (LV), Visual Field Continuity (VFC), Visual Angle Change (VAC), and Visual Depth Penetration (VDP). Through a cross-cultural design experiment—translating the spatial rhythm of a traditional Chinese garden (Lion Grove Garden in Suzhou) into a modern campus layout in Milan—we validated the framework's effectiveness. The experimental results demonstrate that SEEP-driven generative solutions achieved high-fidelity reconstruction of the target spatial sequence (Openness MAD=0.026, Rhythm MAD=0.037) while maintaining full compliance with complex functional requirements (3,264 m² mixed-use program, 100% satisfied). This proves that experience optimization and functional efficiency are not a zero-sum game; AI-driven design can precisely reconstruct the cognitive rhythm of cultural spaces while meeting physical constraints. This research provides a methodological foundation for human-computer interaction in architectural design, advancing the paradigm shift from "geometry-oriented" to "cognition-driven" generative design.
Jason Leo Zhao (Sun,) studied this question.