Analyzing audience mental health through the communication and structure of complex networked online music culture.

This study explores the complexities of online music cultural communication by incorporating the structure of complex networks, aiming to offer more personalized experiences for music creators, platforms, and users engaged in dissemination activities. A novel ABM-NE model is developed by combining Agent-Based Modeling (ABM) with Nonlinear Equations (NE). On the one hand, this model uses ABM to conduct a dynamic simulation of the propagation behaviors of multiple target sources. On the other hand, a nonlinear analysis of the evolution of node states is conducted using NE, which can achieve multi-level modeling and analysis of music culture communication in a complex network environment. Compared with traditional models, the innovation of ABM-NE lies in its ability to dynamically adjust weights and provide real-time feedback on node states. This mechanism effectively compensates for the deficiencies of traditional models in propagation stability and user diversity performance. Key findings include: (1) The relative density index fluctuates between 0.31 and 0.43, while node densities in the Barabási-Albert (BA) and Holme-Kim models range from 0.16 to 0.32. Compared to these traditional models, the proposed model exhibits smaller and more stable fluctuations in node density, even as internal dynamic parameters are adjusted, leading to more balanced dissemination dynamics. (2) Analysis of social influence reveals significant enhancement from user feedback to platforms within the model, reaching a peak of 0.91 at the fifth time step, indicating the broader dissemination effects of the ABM-NE model. (3) Dissemination efficiency analysis shows notable differences in efficiency under varying conditions for the ABM-NE model. High levels of social influence, matching degree, and integration foster faster and wider information dissemination, with weighted dissemination efficiencies of 0.77, 0.77, and 0.8, respectively. This highlights the model's capacity to optimize dissemination strategies. Experimental data show that this model has achieved precise prediction of the dissemination effect on music platforms and can improve the matching degree between content and the audience. Meanwhile, it is helpful for the digital inheritance of traditional music culture and the improvement of the mental health of the audience. Overall, this study aims to unveil the intricate mechanisms of multi-source propagation, node dynamic evolution, and social influence in online music cultural communication. By enhancing the stability of dynamic propagation in complex network environments, the model ensures its adaptability to achieve high dissemination efficiency under diverse conditions. The insights derived from this study are valuable for platforms within the online dissemination system, helping to refine cultural dissemination strategies and strengthen the digital operational capabilities of the music industry.