【Objective】In order to address the complex coupled constraints among multiple design elements and the relatively low efficiency of scheme iteration in landscape architectural plan design, this study developed an automated generative model capable of perceiving multidimensional site-environment data, such as existing-site imagery, circulation routes, and the types and spatial distributions of site elements, while supporting a three-stage workflow for landscape architectural plan design. 【Method】A three-stage framework comprising “preliminary design-scheme modification-master plan design” was established, and a corresponding multidimensional constrained diffusion model (MCDM) was developed. The input and output channels of the model were expanded to enable joint learning from multidimensional data, including RGB site imagery, grayscale circulation maps, and categorical raster data, thereby achieving multidimensional spatial feature fusion and controllable generation. 【Result】In the “preliminary design” stage, the model automatically generated visually realistic site design renderings and corresponding spatial layout schemes based on input data, including existing-site imagery, circulation routes, and the types and spatial distributions of site elements. In the “scheme modification” stage, manually refined spatial-element layouts were introduced as additional constraints to guide the targeted optimization and regeneration of schemes produced in the previous stage, thereby enabling efficient scheme iteration. In the “master plan design” stage, the optimized scheme from the previous stage and the associated spatial-element data were used as multidimensional constraints to generate batches of master plan drawings in a professional hand-drawn style. 【Conclusion】The MCDM can improve the generation efficiency and diversity of landscape architecture schemes, providing a viable technical paradigm for human-machine collaborative design.