Enhancing the Competitive Edge of the Greater Bay Area's Digital Cultural Industry: Value Chain Optimization Based on GA-K-means Algorithm

The purpose of this paper is to discuss the promotion of the value chain of the digital cultural industry in the Greater Bay Area based on the cluster analysis algorithm. First, the relevant background is discussed, and the reasons and purposes of the research are further expressed. It also introduces the digital cultural industry in the Greater Bay Area, and cooperates with the cluster analysis algorithm to improve the value chain of the digital cultural industry in the Greater Bay Area. In addition to sorting out some related work, it also introduces research methods, such as the digital cultural industry value chain model, which includes the digital cultural industry chain structure model and the relationship between the elements of the value chain. Then, the fuzzy clustering analysis algorithm is introduced, and the final optimal algorithm is determined by screening and comparing four methods of standard deviation normalization, maximum value normalization, range normalization and mean normalization. Using similarity measure to improve the algorithm efficiency, it shows the digital cultural industry value chain taking the record industry as an example. Through the comparison of the traditional music record industry value chain and the music record industry value chain under the digital culture, the best industry model is obtained. The method also mentioned the digital cultural value chain model and the hierarchical clustering algorithm process under the cluster analysis method, and then carried out the effect experiment of the cluster analysis algorithm under the Spark environment, built the experimental environment, and tested the data set and clustering algorithm. Comparing the two algorithms, it is concluded that the GA-K-means-L algorithm is better. After that, this paper conducts clustering analysis based on the parallelized clustering algorithm GA-K-means under Spark cluster. It is concluded that in the Spark+ YARN cluster environment, the GA-K-means algorithm has better effect when analyzing the data with large amount of data and high dimension. Then carry out the promotion of the value chain of the digital cultural industry in the Greater Bay Area, classify it through the cluster analysis algorithm, and then improve the value chain, and finally conduct relevant discussions and summaries.