Graphic Language Representation in Visual Communication Design Based on Two-Way Long- and Short-Memory Model

Abstract

With the popularity of neural network research, the application based on neural network model is gradually applied to all aspects of people’s life. Neural network model can not only solve the algebraic problems that traditional machine learning can solve but also recognize and analyze graphics through self-learning. For example, face recognition, web page recognition, product packaging design, and application are inseparable from the dissemination of graphic language. When these processes are realized through computer language, it is necessary to accurately identify these graphic languages. However, traditional machine language learning has poor performance in graphic language learning, which further leads to the application to achieve the purpose of the original visual communication design. Therefore, based on the neural network algorithm, this paper improves a new neural network model—two-way long- and short-memory model to make the computer recognize the graphic language more accurate and further explores the graphic language representation in the visual communication design based on the two-way long- and short-memory model.