Big Data-Driven Intelligent Analysis for Art Design Schemes Based on Grey Correlation

In area of art design, it has been a promising solution to integrate intelligent algorithms to improve working efficiency. However, there still lacks mature solutions that are used for digital evaluation of design works. To deal with such issue, this paper introduces big data analysis to construct prototype for such application. Thus, a systematic research on a big data-driven intelligent system for digital evaluation of design works, is conducted in this paper. Firstly, the grey correlation analysis is utilized to explore the importance of multi-source influential factors. On this basis, a digital evaluation algorithm is developed to output digital evaluation of design works from initial feature data. The grey correlation algorithm is used to optimize the data analysis process, and through preprocessing and accurate data detection, the confidence level of the effectiveness results is improved. Finally, a prototype of the intelligent system in which evaluation algorithm can be embedded, in order to provide a platform for smart operation of such workflow. For empirical analysis, we carry out a case study to verify performance of the proposed technical framework. The traditional methods can have a metric value about 40%, and the proposal can have a metric value about 60%-70%. The results show that the proposal can serve as an available solution to implement digital evaluation of design works, under environment of big data stream.