The Application of Deep Network and Reinforcement Learning for Art Design in Graphical User Interface Wireframe Generation

The development of graphical user interfaces has made significant progress in the past few decades, playing an important role in computer user experience and human-computer interaction. However, at present, there is a lack of professional experienced workers in graphical user interfaces, and the art design of graphical user interfaces has low attention in real life. Therefore, this research introduces reinforcement learning algorithm, combines it with deep network, and realizes automation and intelligence in the generation of art design oriented graphical user interface and graphical user interface wireframe. The test results indicate that the graphical user interface method proposed in this study has average values of 0.075 and 0.869 for the Fréchet inception distance and one nearest neighbor accuracy in the category subset, and 0.070 and 0.823 for the development company subset. The comprehensive average scores for the three indicators of aesthetics, color coordination, and structure in manual evaluation are 3.11, 3.30, and 3.21, respectively. The research proposes a wireframe generation method with average values of Fréchet inception distance and one nearest neighbor accuracy of 0.082 and 0.911, respectively. The average value of position deviation index is 1.018, the average score of manual evaluation is 3.32, and the average values of structural similarity and spatial Euclidean distance are 0.363 and 3.683. The experimental results indicate that the method designed in this study generates a graphical user interface with higher quality than traditional common methods, and is more aesthetically pleasing, in line with popular art aesthetics.