The design of innovative path of inheritance of non-heritage music and art based on big data analysis and cultural inheritance education

As an important carrier of Chinese culture, the inheritance and innovation of non-heritage music faces multiple challenges in the digital era.Using big data analysis and complex network communication dynamics as the core tools, this paper explores the digital communication paths and educational practice strategies of non-heritage music by constructing an information cascade model in social networks.The study first starts from the practical significance of integrating non-heritage music into college education, and clarifies its communication value in cultural heritage and youth groups.It also combines node cascade feature modeling, including temporal relationship and preference similarity analysis and complex network propagation dynamics model (SI, SIS, SIR and threshold model), to quantify the propagation law and diffusion threshold of non-heritage music content in social networks.Based on the empirical data of Weibo and Tik Tok platforms, Monte Carlo simulation and numerical iterative experiments are conducted to reveal the spatio-temporal evolution characteristics and propagation mechanism of non-legacy music information under different network topologies (SF and RR networks).The empirical study and Monte Carlo simulation experiments reveal the spatio-temporal evolution characteristics of non-heritage music information dissemination: structured forwarding dependence (84.46% of the initial forwarding dependence on the attention relationship), the lifecycle bimodal characteristics (two forwarding peaks within 72 hours), and the regulatory mechanisms of the dissemination parameters (, , ) on the diffusion efficiency.Experiments show that the cascade incremental prediction performance of the proposed complex network dynamics model on Aminer, SinaWeibo, and Twitter datasets significantly outperforms that of the existing methods (e.g., MSE is reduced to 2.862, and RMSPE is 0.483), which verifies its potential for application in high-precision prediction and optimization of propagation strategies.