The Influence of Digitization on the Dissemination of Traditional Chinese Music and Weibo Content Propagation Under Deep Learning

This study aims to investigate the dissemination of traditional Chinese music in the digital era and the application of deep learning in predicting the influence of Weibo content propagation. Firstly, the impact of digitization on the dissemination of traditional Chinese music is analyzed. Secondly, based on the Bidirectional Gated Recurrent Unit (Bi-GRU), traditional content feature modeling methods are optimized by introducing content text features. Simultaneously, the Graph Attention Network (GAN) is divided into three steps, allowing it to consider the edge properties of input sequences. The improved content feature modeling, GAN, and multilayer perceptron are integrated to construct a Context-dependent Dynamic Graph Attention Network (C-DGAN). In order to validate the performance of the C-DGAN model, Mean Square Logarithmic Error (MSLE) is used as the evaluation metric in comparative experiments at observation times T=1, 2, 3, and 4 hours. The results indicate that at T=4 hours, C-DGAN achieves an MSLE of 1.854, reducing by at least 0.134 compared to the baseline model, demonstrating superior performance in predicting the scale of Weibo content propagation. Additionally, in comparison with models using different recurrent neural networks, the model employing the Bi-GRU network performs the best. Thus, the proposed C-DGAN model exhibits excellent performance in predicting Weibo content propagation influence. The study findings provide robust support for the study and practice of Weibo content propagation.