## 1 Introduction
### Literature Review
[PERSON] et al. [10] proposed an algorithm based on an encoder-decoder network to generate residential floor plans using the perimeter of the house as an input. 'Graph2 Plan' is another algorithm proposed for floor plan layout generation based on GCNs and CNNs considering the user input layout [11]. Using this algorithm, a more detailed introduction of layout constraints including room adjacencies is possible. Generating vectorized architectural floor plans from rasterized images using deep learning is another widely investigated area. Many researchers [12; 13] have used deep learning algorithmssuch as GANs, ResNet, CNN, and fully convolutional networks (FCNs) to generate vectorized floor plans with high accuracy using rasterized images with varying complexities. There are also some classification studies on the deep learning of architectural design images. For example, [PERSON] et al. [14] developed a training algorithm using a dataset of architectural drawings (floor plans and profiles) based on tagged pixels. After some iterations, the algorithm was able to classify the pixel-based floor plans and sections better. There are also some studies that use AI to identify stylistic features of architects. For example, [PERSON] et al. [15] used the latest techniques in deep learning and computer vision to measure the visual similarity between the architectural designs of different architects. Capable of identifying architectural styles and architect styles well, the algorithm enabled the authors to identify individual architects with 73% validity, and the clustering analysis mostly agreed with traditional descriptions by architectural historians and theorists. This study shows that predictions for interior scenes are more accurate than for exterior scenes. There are also some studies on deep learning related to architectural design regarding users' perceptions of buildings. For example, [PERSON] et al. [16] proposed a method for local spatial component recognition using 3D isometric methods and convolutional neural networks (CNNs) to investigate how to quantitatively and effectively describe the spatial experience of observers. [PERSON] et al. [17] investigated the use of deep convolutional neural networks to quantify the legibility of interior spaces and achieved a top-1 accuracy of 98% on a test set. This technique can be further applied to indoor localization with its low cost and fast deployment speed. [PERSON] et al. [18] used machine learning to learn photos inside brand flagship stores to obtain a system that includes interior design styles. [PERSON] et al. [19] innovatively used a user's point of view and perspective, and proposed that the semantic segmentation model can be used to identify and segment a streetscape map to obtain indicators such as sky closure and greening rate. This method was inevitably more consistent with the underlying logic of urban feature extraction, as well as being more rigorous and reliable. As can be seen from the above literature review, there are a few existing studies on deep learning and architectural design, mainly focusing on identifying and generating architectural design images, analyzing spatial features, simulating users' feelings toward a building, etc., and predicting the results using AI, as well as optimizing and improving the resultant scenarios. However, there are fewer studies that apply deep learning to architectural functional spaces while focusing on the ability of architectural functional spaces to enhance the quality of life of users, reduce the consumption of energy and resources, and understand how buildings can support specific activities and needs. The construction of rail transit is closely related to people's lives. The internal space of metro stations is complex, and the functional division is relatively uniform, so it has the characteristics of \"modularity\"; therefore, optimizing its spatial layout is of great practical significance.