The use of architectural morphological analysis and generative design is an important strategy to interpret current designs and to propose novel ones. Conventional morphological features are defined based on qualitative descriptions or manually selected indicators, which include subjective bias, thus limiting generalizability. The lack of public architectural morphological datasets also leads to setbacks in data-driven morphological analysis. This study proposed a new method for generating topology-based synthetic data via a rule-based system and for encoding morphological information to promote morphological classification via deep learning. A deep convolution network, LeNet, which was modified in the output layer, was trained with synthetic data, including five spatial prototypes (central, linear, radial, cluster, and grid). The performance of the proposed method was validated on 40 practical architectural layouts. Compared to the ground truth, the proposed method provided an encouraging accuracy of 97.5% (39/40). Interestingly, the most possible mistakes of the LeNet were also understandable according to the architect's intuitive perception. The proposed method considered the statistical and overall characteristics of the training samples. This work demonstrated the feasibility and effectiveness of the deep learning network trained with synthetic architectural patterns for morphological classification in practical architectural layouts. The findings of this work could serve as a basis for further morpho-topology studies and other social, building energy, and building structure studies related to spatial morphology.

建筑形态分析和衍生式设计的使用是解释当前设计和提出新颖设计的重要策略。传统的形态特征是基于定性描述或手动选择的指标来定义的，其中包括主观偏见，从而限制了普遍性。公共建筑形态数据集的缺乏也导致数据驱动形态分析的挫折。本研究提出了一种新方法，通过基于规则的系统生成基于拓扑的合成数据，并通过深度学习对形态信息进行编码以促进形态分类。在输出层修改的深度卷积网络 LeNet 使用合成数据进行训练，包括五个空间原型（中心、线性、径向、集群和网格）。所提出方法的性能在 40 个实际建筑布局上得到了验证。与地面实况相比，所提出的方法提供了令人鼓舞的 97.5% (39/40) 的准确率。有趣的是，根据建筑师的直觉，LeNet 最可能出现的错误也是可以理解的。所提出的方法考虑了训练样本的统计和整体特征。这项工作证明了用合成建筑模式训练的深度学习网络在实际建筑布局中进行形态分类的可行性和有效性。这项工作的发现可以作为进一步的形态拓扑研究和其他与空间形态相关的社会、建筑能源和建筑结构研究的基础。