Design of an Intelligent Classification Model for Interior Design Knowledge Graph Based on Simulated Annealing Algorithm

Abstract

In real life, interior design is a complex and challenging job. Interior design solutions need to consider factors such as spatial layout, color matching, etc., and the emergence of knowledge graph provides a new method of summarizing design ideas for the interior design industry. However, in the face of a large number of knowledge graphs, how to achieve high-quality classification of knowledge graphs has become a hot topic of discussion in related industries. The work builds a knowledge graph intelligent classification model based on machine learning, simulated annealing, and genetic algorithms to accomplish effective knowledge graph classification. The global optimization of convolutional neural network parameters is accomplished by merging the model using the simulated annealing approach and the genetic algorithm. The experimental results indicated that the proposed model converged to an F1 score of about 95.03%, while the control model converged to an average F1 score of 94.37% and 94.26%. The average recall of the proposed model was 91.71% while the average recall of the control model was 87.06%. Based on the experimental findings, it can be said that the suggested model performs noticeably better than the control model, indicating that it is an improved knowledge graph classification method. In addition, the proposed model contributes to the development of interior design related industries.