Department of Marxism, Northeast Normal University, Changchun 130024, China.
Beijing University of Chemical Technology, Beijing 100029, China.
J Environ Public Health. 2022 Sep 1;2022:1231601. doi: 10.1155/2022/1231601. eCollection 2022.
Popularizing contemporary Chinese Marxism is urgently needed in order to support the ongoing development of socialism with Chinese characteristics as well as the inherent necessity of Marxism. This essay views the popularization of Marxism as a turning point in the new media environment. It examines the necessity and reality of this popularization in the new media era, considers the new development needs of the popularization of Marxism in propaganda, and further unearths the original construction concepts of the popularization of the Marxism propaganda network. In parallel, a Marxist learning platform is built using data mining technology. Studies reveal that this algorithm has a high clustering accuracy and a recall rate that is about 6% higher than DECluster's. Additionally, this algorithm takes less time to execute under the same scale transaction set. This demonstrates the superior performance of this algorithm. The user's learning record and learning interests can be formed into an intuitive law using the algorithm presented in this study, which can be used to analyze and calculate the user's learning content related to Marxism. This law can then be used to assist the user in creating a customized learning plan for Marxism.
为了支持中国特色社会主义的不断发展和马克思主义的内在需要，急需普及当代中国马克思主义。本文将马克思主义的普及视为新媒体环境下的一个转折点，探讨了在新媒体时代普及马克思主义的必要性和现实性，思考了宣传中马克思主义普及的新发展需求，进一步挖掘了马克思主义宣传网络原初建构理念。同时，利用数据挖掘技术构建了一个马克思主义学习平台。研究表明，该算法具有较高的聚类准确率和召回率，比 DECluster 高出约 6%。此外，在相同规模的事务集下，该算法执行所需的时间更少。这表明该算法具有优越的性能。可以使用本研究中提出的算法将用户的学习记录和学习兴趣形成直观的规律，用于分析和计算用户与马克思主义相关的学习内容。然后，可以使用该规律来帮助用户制定马克思主义的个性化学习计划。
