Intelligent interior decoration style recommendation and customization system based on deep learning style transfer algorithm

With people’s pursuit of personalized lifestyle, the choice of interior decoration style has increasingly become a key factor in consumers’ decision-making. Traditional interior decoration design often relies on hand-made design, which is time-consuming and lacks personalization and innovation. In recent years, the rapid development of deep learning technology has introduced new solutions in the field of interior decoration. Specifically, the successful application of style transfer algorithms in image processing has provided strong technical support for the intelligent recommendation and customization of interior design styles. This paper proposes a system framework for the intelligent recommendation and customization of interior decoration styles based on the deep learning style transfer algorithm. It also discusses the challenges consumers face when choosing from a large number of design styles. The system combines convolutional neural network and generative adversarial network, adopts style transfer algorithm, and integrates various decoration fashions and user preferences to achieve intelligent recommendation. Through the training of a large number of decoration image data sets, the system can identify and extract interior space style features, and then create personalized decoration suggestions according to user needs. After adopting the decoration scheme recommended by the system, the satisfaction of users increased by about 20%, and the execution efficiency of customized design increased by 15%. Data analysis shows that using the deep learning style transfer algorithm, the system prediction accuracy rate reaches 85%, providing users with more accurate decoration choices that meet individual needs.