The Cultural Value Validity of Digital Media Art Based on Deep Learning Network Model

Abstract

The development of Internet digital media technology has enabled more works of different artistic styles to be discovered, learned, and appreciated by art lovers. Artists generate new creative ideas from different artistic painting styles, resulting in many artistic creation styles that are mixed with different artistic creation techniques. However, in the face of an increasing number of digital media art works, the identification of artistic and cultural value is mainly done manually by professionals, which costs a lot of human and financial resources. Therefore, it is of great practical significance to study how to efficiently and accurately classify various types of artistic images to help users select images that meet their needs. In order to solve this problem, this paper proposes a deep learning neural network model based on a dual-core compression activation module. The convolution kernels of different sizes in one module are used to extract the overall features and local details of the image, and another module is used to achieve the main goal. The enhancement of features and the suppression of irrelevant features realize the evaluation of artistic and cultural value. The experimental results show that, compared with mainstream neural network models and traditional classification algorithms, the proposed algorithm has higher classification accuracy and higher recognition and classification efficiency.