The application of deep learning algorithm in intelligent optimization of architectural planning spatial layout

In order to improve the intelligent optimization effect of architectural planning spatial layout, this paper combines deep learning algorithms to propose a building roof style recognition method and design model based on salient region suppression and multi-scale feature fusion (SRSMFF). Aiming at the problem of incomplete feature extraction of architectural elements, this paper designs a salient region suppression module (SRSM). Aiming at the problem of data processing redundancy of various buildings, this paper proposes a multi-scale feature fusion method (MSFF), which extracts architectural element information by fusing different resolution feature maps. From the experimental results, it can be seen that the building segmentation effect of SRSMFF with complex boundary contour is more suitable for the building shape than other test methods. Moreover, the model proposed in this paper can effectively reduce system redundancy and training loss in building space planning and layout, thus effectively improving system work efficiency. In addition, it can perform more optimization operations in a limited time, and the system design evaluation is as high as 90 points or more. Therefore, the deep learning fusion algorithm proposed in this paper can not only provide reference for architectural planning, but also provide channels for the intelligent layout of indoor buildings and promote the intelligent development of subsequent architectural design.