Research on resume screening and career matching model based on fuzzy natural language processing

With the rapid development of the information age, traditional resume screening methods have been difficult to meet the urgent needs of enterprises for efficient and accurate talent selection when dealing with massive recruitment data. Based on this, this study proposes a resume screening and career matching model based on Fuzzy Natural Language Processing (Fuzzy NLP), which aims to optimize the intelligent matching process between talents and positions by using cutting-edge technology. The model deeply integrates fuzzy logic theory and semantic analysis technology, and constructs a multi-dimensional semantic understanding framework to refine the analysis of resumes and job descriptions, effectively eliminate the semantic ambiguity and uncertainty in natural language, and break through the bottleneck of traditional methods at the level of semantic understanding. In the empirical research phase, the research team systematically tested a real-world dataset of 5000 resumes and 1000 job information. Experimental results show that compared with the traditional keyword matching method, the proposed model achieves a significant improvement of 25% to 85% in the matching accuracy, and accurately captures the potential relationship between job requirements and candidates’ abilities through dynamic weight adjustment and semantic similarity measurement mechanism. In terms of screening efficiency, the model helps optimize the recruitment process, shortens the manual review time by 30%, and significantly reduces labor and time costs. Especially when dealing with fuzzy semantic descriptions and polysemy word scenarios, the model reduces the false positive rate by 20% through context perception and fuzzy rule reasoning, showing excellent semantic understanding ability and robustness. This study not only verifies the practical value and technical advantages of fuzzy natural language processing technology in the field of human resource management but also provides theoretical support and methodological reference for promoting the intelligent transformation of the recruitment process, which is expected to build a more efficient and accurate talent screening system for enterprises and help human resource management move towards a new stage of intelligent and scientific development.