Data-Driven Dynamic Neural Programming for Network Media Nonlinear Visual Communication Design

Abstract

As network media data gradually enters the field of visual communication design as information, more scholars have begun to rationally study the relationship between visual communication and network media. This paper mainly studies how to use data-driven nonlinear dynamic neural programming method to realize visual communication design in network media environment. The study constructs a network media visual communication design system from three aspects: data-driven, design process realization, and system flow establishment. The network media visual communication design is realized through the five process stages of data: analysis, definition, integration, visual presentation, and rendering. Based on the optimal control of visual communication design, the evaluation of visual communication realization and the dissemination of visual communication information are further analyzed, and the following three conclusions are drawn: firstly, each factor does not exist alone; they influence each other. The system digs out the association rules of different factors that affect the visual communication design under the standard of 10% support and 85% confidence; secondly, comparison of the output value of the system with the real output value of the object is found that they are very close, which shows that the dynamic neural programming method is more complex and nonlinear. Finally, through the simulation of the relationship between the continuous dissemination of information and the number of tourists, on the first day, there was the first burst of new customers. The first outbreak occurred on the fifth day of the first week. In the first month, as the number of tourists increased, the dissemination of tourist information by tourists also gradually increased. This fully shows that there is always a long-term relationship between information dissemination and audiences.