Analysis of the Dissemination Path of Marxist Classical Theory Based on Data Visualization Technology

Abstract

This study applies data visualization technology to analyze the dissemination paths of Marxist classical theory within complex social networks. Integrating communication theory, social network analysis, and visualization techniques, the research constructs a multidimensional analytical framework to examine how Marxist theory propagates across different network structures. Based on simulation experiments involving diverse audience groups and dissemination channels, the study identifies the differentiated roles of academic, political, and mass media nodes in shaping propagation speed, coverage, and influence. The results show that scale-free networks generally exhibit faster earlystage diffusion and higher overall coverage under hub-dominated conditions, while small-world networks may achieve comparable or superior early propagation speed when initial propagators are embedded in highly clustered communities. These findings highlight the conditional effects of network topology and node roles on ideological dissemination. The study demonstrates the value of data visualization in revealing structural mechanisms of theory diffusion and provides methodological reference for network propagation analysis, information-flow modeling, and signal-diffusion studies in complex communication environments.