Research on Resume Automtic Analysis and Matching Technology Based on Machine Learning and Natural Language Processing

In the process of enterprise recruitment, the traditional resume screening method is inefficient and easily influenced by subjective judgment. In this study, machine learning algorithm is used to extract key information from unstructured resume text, and natural language processing (NLP) technology is used to deeply understand the semantic content of resume text, so as to improve recruitment efficiency and matching quality. The system architecture design includes data acquisition, preprocessing, feature extraction, model training, prediction analysis, feedback and optimization modules, forming a complete closed loop. Key technologies include machine learning algorithms such as random forest (RF) and support vector machine (SVM), and NLP technologies such as text segmentation, named entity recognition (NER) and word embedding. The experimental results show that SVM model is superior to RF and traditional keyword matching methods in accuracy, recall and F1 value, which shows its advantages in the task of matching resumes and positions. This study provides a new solution for the automatic matching of career platforms, which is helpful for enterprise recruiters to quickly screen out candidates who are highly matched with job requirements and provide job seekers with more accurate job matching.