Author(s)

Yuxin Zhu ¹

Affiliation(s)

¹ School of Foreign Studies, China University of Political Science and Law, Beijing, China

Corresponding Author

Yuxin Zhu

ABSTRACT

Accurate employment-status prediction is essential for targeted labor-market services, especially when individual socio-demographic attributes, household conditions, and industrial restructuring jointly shape employment opportunities. Based on an anonymized survey from Yichang, China, this study develops a data-driven framework for employment diagnosis, prediction, and person-job matching. After data screening, 4,980 valid observations were retained. A binary logistic regression model was first used to identify interpretable determinants of employment status, including gender, age, disability status, education, household registration, residence pattern, family size, and industrial-structure indicators. Machine-learning classifiers were then compared under an imbalanced-label setting, including logistic regression, decision tree, random forest, Naive Bayes, and support vector machine. The results show that education and age are strong positive predictors, while larger household size and some structural-transition factors are associated with lower employment probability. After resampling and parameter tuning, SVM and random forest achieved the best predictive performance, with accuracy of 0.93, recall of 0.97, and F1-score of 0.95. Finally, a resume-job matching module is proposed by combining resume parsing, skill ontology, and weighted similarity ranking. The study contributes an integrated and interpretable workflow for regional employment monitoring, risk identification, and personalized job recommendation.

KEYWORDS

Employment status prediction; binary logistic regression; support vector machine; random forest; imbalanced classification; job matching

CITE THIS PAPER

Yuxin Zhu. An Interpretable Machine-Learning Framework for Employment Status Prediction and Job Matching. Information Systems and Economics (2026). Vol. 7, No.1, 101-110. DOI: http://dx.doi.org/10.23977/infse.2026.070112.

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