The persistent shortcomings of conventional residential design, rooted in the static partitioning of space into predetermined “functional rooms”, underscore an urgent need for a paradigm shift toward more adaptive and behaviorally responsive architectural solutions. This study addresses this challenge by proposing a fundamental reorientation in spatial organization: from static, function-based zoning to dynamic clustering derived from the correlations between micro-user behaviors. The primary objective is to enhance both the precision of spatial division and the dynamic adaptability of residential layouts through a novel, quantitative design methodology. To achieve this, the research integrates the Design Structure Matrix (DSM) and the Fuzzy C-Means (FCM) clustering algorithm, constructing a scientifically rigorous framework to translate behavioral patterns into optimal spatial modules.Current research in residential spatial organization follows two predominant paths, each with significant limitations. First, a component-based path, focuses on the standardization of physical elements like furniture and building components. While it provides a clear, hierarchical system for spatial decomposition and addresses “how to construct precisely”, it inherently treats space as an assembly of static objects, failing to respond to the dynamics of user behavior and needs. Consequently, it cannot answer “why this organization is optimal”. Second, a behavior-based path, seeks to establish spatial modules by analyzing behavioral units and their spatiotemporal patterns. However, this approach has largely remained qualitative or conceptual, constrained by limitations in behavior data acquisition and the lack of a quantitative, computable model to effectively convert behavioral insights into spatial design. This gap often forces designers to revert to empirical “functional bubble diagrams” without genuine scientific generation capabilities. Fundamentally, the prevailing paradigm remains one of “naming rooms for functions” rather than “organizing space for behaviors”. To bridge this critical gap, this study develops and applies an integrated analytical model combining DSM and FCM. The process begins with the deconstruction of traditional functional rooms into a fine-grained spectrum of micro-behaviors. A comprehensive database of residential behaviors is constructed, alongside a four-dimensional spatial correlation index model that quantifies the relationships between these behavioral spaces. The DSM is employed to systematically map and structure these correlations. Subsequently, the FCM clustering algorithm processes this quantitative dataset, grouping the subdivided behavioral spaces into modules based on the strength of their interconnections. The clustering is optimized to identify the scheme with the highest modularity, ensuring that the resulting groupings exhibit strong internal cohesion and clear external boundaries. Applied specifically to residential indoor space design, the method decomposes activities into 35 distinct micro-behavioral types. Through the computational clustering process, these are synthesized into 15 optimized general behavioral space modules (e. g., modules integrating closely linked behaviors that might traditionally be separated across a “kitchen”, “dining”, and “storage” room). A comparative evaluation between these newly generated modules and traditional functional zoning reveals the clear superiority of the behavior-correlation strategy. The new modules demonstrate significant advantages across multiple quantitative metrics: enhanced modularity (indicating betterinternal coherence), greater typological diversity, improved combinability for flexible layout generation, and a more rational hierarchical structure. This evidence effectively verifies that a clustering strategy driven by behavioral correlations yields a spatial organization system with inherently better design properties than one based on a priori functional labels.The core contribution of this research is the proposal, development, and validation of the “behaviorcorrelation clustering” method as a scientifically robust alternative to traditional functional zoning. By shifting the foundational unit of design from the room to the behavior pattern, the study provides a quantifiable and reusable technical support system for modular architectural design practice. It advances spatial organization theory by introducing a model that clarifies behavior types, quantifies spatial precision, and scientifically manages correlations. The constructed hierarchical structure of behavioral space offers a practical reference framework for architectural design, one that can be applied to modular product design and continuously refined with dynamic market or user data.Finally, this study establishes a generalizable model for behavioral space analysis. Future research will focus on the empirical validation of design quality improvements through application in typical residential units, assessing outcomes in spatial efficiency, behavior-flow fit, and user adaptability. Furthermore, the model’s flexibility allows for the integration of survey data from specific user groups as new parameters, enabling highly customized spatial design and significantly expanding the methodology’s applicability. This pathway promises to transform residential space design from a practice of static compartmentalization into a responsive, user-centered, and scientifically-informed process.