Research on front-end interface adaptive design algorithm based on deep learning

With the diversification of smart devices and screen sizes, front-end interface adaptive design has become an important research direction to enhance user experience. Traditional responsive layout methods mainly rely on rule setting and manual adjustment, which are difficult to cope with complex user interaction requirements and dynamically changing interface layout. In order to solve this problem, this paper proposes a front-end interface adaptive design algorithm based on deep learning, which makes use of neural networks to automatically adapt and optimize different devices, screen sizes and user behaviors, so as to improve the degree of intelligence of interface design. This paper first analyzes the feasibility of deep learning in interface adaptive design and discusses the current progress of related research. On this basis, an adaptive layout generation model combining convolutional neural network (CNN) and recurrent neural network (RNN) is designed to achieve automatic adaptation and optimization of interface elements. In this paper, a set of experimental environments is constructed to test and evaluate the proposed method, and the results show that the method outperforms the traditional adaptive design strategy in terms of adaptability, interface integrity and response speed. This paper summarizes the research results and looks forward to the future development direction of front-end interface adaptive design based on deep learning.