In recent years, the research on Marxist theory has received a lot of attention. Many studies use quantitative research methods such as statistical keywords and constructing knowledge map to carry out Marxist theoretical analysis. In this paper, by constructing a self-expanding Chinese word separation and self-expanding address data knowledge graph, matching Marxist Chinese addresses based on the full-text indexing knowledge graph, incorporating a weighted pinyin full-text search mechanism to improve the matching accuracy of misspelled addresses, constructing a multiple matching mechanism for Marxist addresses by combining an online geographic parsing interface, and performing semisupervised matching for a small number of difficult addresses, a complete system of Marxist address matching methods is formed. By studying and analyzing the basic orientation and attention to academic hotspots, we can gain insight into the characteristics, rules, and problems of academic hotspots in Marxist theory journals.
Most of the existing studies focus on the analysis of college student users of blogs, virtual communities, and other online application platforms, while the research on the motivation of micro-blogging college student users focuses on the research of usage motivation. In this paper, we established a multilevel psychological early warning model based on personality, mood, and emotion space and mapped personality, mood, and emotion to accurately simulate the law of human emotion change for the relevance of micro-blog sentiment analysis to college students' growth development and management. The experiment shows that the method effectively realizes the analysis and description of micro-blog emotion; finally, the designed micro-blog emotion early warning system realizes the visual analysis and timely warning of the psychological condition of the observed subjects, which verifies the effectiveness of the research method.
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