The size of human resources in universities is greatly impacted by the applied study of resource scale, yet the issue of improper scale placement persists. In terms of university human resource size, the application research challenge remains unsolved by standard genetic algorithms, and the results are unsatisfactory. Hence, this study explores the existing literature on the topic and suggests future data-mining-based application research on university human resource scale. First, the influencing elements are located using statistical set theory. The indicators are then segregated according to the needs of resource scale application research in order to decrease interference factors. The process of human resources application has been improved, including personnel selection and job matching, and personnel recruitment, with an accuracy rate of more than 98%. Moreover, in the process of overall personnel planning, the planning content and planning conditions also meet the corresponding requirements. Therefore, it can improve the overall level of human resources management, which is more than 40%, and can promote manpower.