Intangible Cultural Heritage Management Using Machine Learning Model: A Case Study of Northwest Folk Song Huaer

Abstract

Machine learning is the core field of artificial intelligence. It provides a guarantee for the digital intangible cultural heritage management of artificial intelligence. However, the existing theoretical and practical research points out that there are still many gaps in this field. Huaer is a folk song popular in Qinghai, Gansu, Ningxia Hui Autonomous Region, and individual regions of Xinjiang. It is known as the soul of the northwest. It is a national human intangible cultural heritage. It was listed as human intangible cultural heritage by the United Nations in September 2009. With the rapid development of network technology and machine learning, it is very important to manage the network communication and deep mining of Huaer information. In this regard, use of machine learning natural language processing to mine the information of Huaer lyrics is proposed. By constructing the Huaer model of recurrent neural network (RNN), data mining of Huaer lyrics is carried out, and the built-in language module in Python is interconnected with dynamic Web pages. Four Huaer image segmentation methods and five deep learning algorithms are proposed, and the steps of image segmentation algorithm and BP neural network algorithm based on block technology are introduced. The results can provide new ideas for the protection and inheritance of music intangible cultural heritage and provide effective and high-quality information for Huaer art researchers and lovers.