Application Research of Graphic Design Based on Information Resource-Sharing and Big Data Technology

Author:

Xu Dan12ORCID

Affiliation:

1. Department of Architecture and Art Design, Nanjing Vocational Institute of Railway Technology, Nanjing 210015, China

2. School of Arts, University Sains Malaysia, George Town 11800, Malaysia

Abstract

The field of graphic design is an important industry rising in recent years if a new graphic design solution requires the designer to design from the ground up, it will consume a lot of time and material resources. The information resource-sharing platform already has many element characteristics to provide the designer to carry on the reference, and this will greatly save the designer time and the material resources. The traditional graphic design method will consume some resources only by relying on the designer and the solutions designed by this method may not be innovative enough. This research will design a graphic design system and management method from the point of data and big data of information resource-sharing platform. The results show that the IRM platform can obtain more effective successful cases of graphic design feature data. The clustering method and CNN method can effectively deal with the pattern feature, color feature, shape feature, and character feature of graphic design. It can not only effectively analyze the feature data value of graphic design but also fits well with the trend of data values. This is a valuable research work for graphic designers. The largest prediction error is only 2.34%, and this part of the error mainly comes from the prediction of pattern features of graphic design. All other forecast errors are within 2.03%.

Funder

2021 Jiangsu Higher Education Reform Project

Publisher

Hindawi Limited

Subject

General Engineering,General Mathematics

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Extraction and Reconstruction of Traditional Art Visual Elements by Graphic Design Incorporating Deep Learning;Applied Mathematics and Nonlinear Sciences;2024-01-01

2. Preface: A Data-driven Volumetric Prior for Few-shot Ultra High-resolution Face Synthesis;2023 IEEE/CVF International Conference on Computer Vision (ICCV);2023-10-01

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