Design and Optimization of Indoor Space Layout Based on Deep Learning

In order to explore the technical path of using artificial intelligence deep learning algorithm in realizing the interior space layout, this study introduces neural network modules such as 3D spatial convolutional (3DSC) neural networks and fuzzy neural networks (FNN), and a deep learning algorithm of indoor spatial layout design (ISLD) based on the adversarial neural network (ANN) is formed. In the algorithm design, a controllable data-interference adverse variation algorithm based on a random number generator is introduced, to obtain the data variant optimization process of genetic algorithm in neural network deep learning. As shown in the simulation analysis, the algorithm yielded significantly better subjective audience evaluation than other algorithms mentioned in references, and because it can be run offline on a single PC workstation, the demand for network resources and computing power resources is relatively small, so under the premise of the same hardware facility investment, higher production capacity can be obtained to get a higher input-output ratio, and it has a certain industry-university-research transformation and market promotion value.