Aesthetic Value Evaluation for Digital Cultural and Creative Products with Artificial Intelligence

Abstract

The domestic cultural and creative industry has abundant resource advantages and broad development space. The design for cultural and creative products has evolved rapidly with the objective to improve its quality. The cultural and creative industries have seen rapid growth in the recent years wherein digital technologies have been incorporated with the traditional methodologies. The digital cultural and creative aspect acts are extremely important in the dissemination of traditional culture on the network platform. This is also supported by the state vigorously to implement innovative industrial policies. However, the adoption of digital technology in the cultural and creative industry is a novel approach. But there exists lack of understanding in terms of its nature and development protocols. It is thus necessary to study relevant theories to guide the development of digital cultural and creative industry. The increasingly prosperous aesthetic culture, especially development for cultural and creative industries, has comprehensively improved aesthetic value of the cultural and creative products. Therefore, the methods to evaluate and realize the aesthetic value of digital, cultural, and creative products are extremely important and relevant in the present day and age. In this study, neural network is used to design an improved back propagation (BP) network in order to evaluate the aesthetic value of digital cultural and creative products. At the outset, the basic idea, structural characteristics, the learning algorithm, and its flow of functioning in the BP network are analyzed. Then, an aesthetic value evaluation model of digital cultural and creative products with BP network is developed. Next, considering the shortcomings of BP network, a segmentation adaptive strategy is used to improve the view field and step size for artificial fish swarm algorithm (AFSA). Finally, the improvised algorithm is verified wherein the simulation results reveal improvement in algorithm convergence speed as well as improvement in optimal solution accuracy as part of the adaptive improvement approach.