In recent years, there has been increasing concern about the effects of indoor thermal environments on human physical and mental health. This paper aimed to study the current status of the thermal environment and thermal comfort in the classrooms of Northeastern University during the heating season. The indoor thermal environment was analyzed with the use of field measurements, a subjective questionnaire, regression statistics, and the entropy weight method. The results show that personnel population density is an important factor affecting the temperature and relative humidity variations in classrooms. The results also show that the temperature and relative humidity in a lecture state are respectively 4.2 °C and 11.4% higher than those in an idle state. In addition, in university classrooms in Shenyang, the actual thermal neutral temperature is 2.5 °C lower than the predicted value of the Predicted Mean Vote. It was found that increasing indoor relative humidity can effectively improve the overall thermal comfort of subjects. Furthermore, the temperature preference of women was higher than that of men. Therefore, when setting the initial heating temperature, the personnel population density and sufficient indoor relative humidity have been identified as the key factors for improving the thermal environment of the classroom.

Rapid changes in vertical illuminance trigger visual fatigue. Therefore, controlling the illuminance ratio of adjacent spaces can ensure the satisfaction and comfort of users. This study takes reaction time as the measure of adaptation and explores the correlation between visual adaptation and comfort in different light environments. The Landolt C ring was selected as the visual standard for the experimental test, the degree of visual comfort was assessed using a Likert scale, and experimental parameters were formulated according to relevant criteria. By analyzing the subjective visual comfort, visual task performance and physiological evaluations of the participants under different changing illuminance levels, we have concluded that there is a significant correlation between reaction time and visual comfort, and no significant effect of gender on visual comfort. Therefore, under the condition of meeting the required value of illumination standard, the smaller the illuminance ratio of adjacent rooms, the more the comfort and visual acuity of users can be guaranteed, and visual fatigue can also be avoided. The study is a useful resource for improving comfort and pleasure in a light environment as well as for lighting design.

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This paper aims to study the generation of spatial function layout of subway stations assisted by deep learning, and train the point cloud data of subway stations based on the Pointnet + + model in deep learning. The point cloud data of subway stations comes from the subway station data of large and medium-sized cities collected and processed by the author. After training and verification, the following conclusions are drawn: (1) It has been verified that the spatial deep learning of buildings in the form of point clouds is highly feasible, and the point cloud format of the architectural space model is fully compatible under the Pointnet + + model. (2) Verified the effectiveness of Pointnet + + for semantic segmentation and prediction of subway station cloud information. The results show that the predicted data has 60%+MIOU (MeanIoU average intersection and union ratio) and 75%+Acc (Accuracy). This paper uses an interdisciplinary research method to combine deep learning of 3D point cloud data with architectural design, breaking through the current situation of using 2D images as research objects, and avoiding the application of "deep learning" to 2D images. Objects cannot accurately describe the limitations of 3D space, providing architects with more intuitive and diverse design assistance.

In recent years, deep learning methods have been used with increasing frequency to solve architectural design problems. This paper aims to study the spatial functional layout of deep learning-assisted generation subway stations. Using the PointNet++ model, the subway station point cloud data are trained and then collected and processed by the author. After training and verification, the following conclusions are obtained: (1) the feasibility of spatial deep learning for construction based on PointNet++ in the form of point cloud data is verified; (2) the effectiveness of PointNet++ for the semantic segmentation and prediction of metro station point cloud information is verified; and (3) the results show that the overall 9:1 training prediction data have 60% + MIOU and 75% + accuracy for 9:1 training prediction data in the space of 20 × 20 × 20 and a block_size of 10.0. This paper combines the deep learning of 3D point cloud data with architectural design, breaking through the original status quo of two-dimensional images as research objects. From the dataset level, the limitation that research objects such as 2D images cannot accurately describe 3D space is avoided, and more intuitive and diverse design aids are provided for architects.