Application of Natural Language Processing in Intelligent Analysis of Marxist Thought on Agriculture, Rural Areas and Farmers

Abstract

The large scale and decentralized nature of Marxist literature on agriculture, rural areas, and farmers make manual analysis inefficient and limit systematic investigation of theoretical evolution and practical relevance. This study develops an intelligent document-parsing framework based on natural language processing. First, BERT is used for semantic encoding and topic classification of documents concerning land systems, farmers’ rights, agricultural modernization, and rural development. Second, named entity recognition is applied to extract key concepts, figures, time nodes, and policy documents, while dependency parsing and graph databases are used to construct a knowledge graph. Finally, TextRank and LDA are combined to generate document summaries and reveal the evolution of themes across historical periods. Experimental results show high topic classification accuracy and strong entity-extraction performance. The proposed framework improves knowledge discovery efficiency and provides an interpretable semantic signal-processing method for intelligent analysis of large-scale ideological and agricultural policy texts.