In the context of digital technological change and media convergence, how to quantitatively analyze the digital communication path of contemporary politics and Marxist thought has become a new problem to be solved. The objective of this paper is to create a model for analyzing the digital communication path of political information and Marxist thought. The degree correlation in the social network algorithm is used to analyze the correlation between the information nodes of contemporary politics and Marxist thought, and the clustering coefficients are used to predict the behavior and evolution of the nodes in the dissemination network. The dissemination model of contemporary politics and Marxist ideology can be explored by combining the infectious disease information dissemination model on the network platform. After the construction has been completed, the model is employed to evaluate the relevant information of a network platform. The early stages of the spread of political and Marxist-related information resulted in 180,000 crowd contacts becoming information exposers and quickly reaching the peak point. The peak point of 119,800 people is quickly reached when both intentional and unintentional communicators of political messages increase. Node density reaches 0.94 and then declines roughly 24 hours after the political message is released. The model employed in this paper is capable of effectively analyzing the digital dissemination path of politics and Marxist ideology and providing a reference for exploring the digital dissemination of political information.