Generative AI technologies have rapidly advanced in recent years, finding extensive applications in architectural design, including the generation of building volumes, which represent the three-dimensional form of buildings. However, current studies in this area lack scientific dataset classification methods and primarily focus on single generative models, with limited emphasis on model integration. To address these challenges, this study presents a comprehensive workflow that integrates multiple spatial computation methods to calculate the feature data of building volumes in urban areas, followed by cluster analysis to classify the building volume dataset. A generative model, combining Conditional Generative Adversarial Networks (CGAN) and Denoising Diffusion Probabilistic Models (DDPM), is introduced to improve the accuracy and diversity of generated building volume point clouds. The workflow is validated through a case study of the old town of Kashgar, demonstrating its effectiveness and robustness. Results show that the trained model achieves a mean training accuracy of 93.88% and a mean validation accuracy of 89.26%, with the training loss stabilizing at approximately 0.1 after 176 epochs. This approach accurately classifies the building volume dataset and generates diverse 3D point clouds of building volumes, highlighting its potential for the generative design of building masses in complex urban neighborhoods. This study offers new strategies for enhancing the design process using AI technologies.
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