Fast search of art culture resources based on big data and cuckoo algorithm

Abstract

The arrival of the era of big data has a great impact on the development of various industries in the society. There are abundant fine arts and cultural resources in the world, but its search is difficult and inefficient. Therefore, the rational development and utilization of artistic and cultural resources are to provide high-quality art and cultural products. At the same time, it is also an inevitable choice to accelerate the transformation of old and new. Power is to cultivate new forms of art and culture. In the context of big data, the cuckoo search algorithm is easy to implement due to its high efficiency. The parameters are rarely studied by various scholars and have been applied to solve optimization problems and search optimization problems. The application results show that it has relatively good performance. Big data searches for the context of artistic culture and artistic resources, whether in traditional painting, sculpture, technology, or the construction of knowledge and technology. Emerging design, photography, video, and the future of visual arts and phenomena, everyday life can build and convey personal attitudes, beliefs, and values of various visual images. Its search efficiency is not high and its accuracy is reduced. In order to solve the above problems, a cuckoo search algorithm (CFCS) based on change factors is proposed in the context of big data. Through data analysis and experiments with Matlab software, the results show that the overall convergence speed of the cuckoo search algorithm based on change factor is obviously better than that of the cuckoo search algorithm. Under the corresponding fitness conditions, the number of iterations of CFCS is significantly less than CS. The search efficiency of CFCS is higher than CS. The accuracy of CFCS is also significantly higher than CS.