AI-Based Employment Situation Analysis of Innovative Talents in the New Media Era

Abstract

As the number of undergraduates increases, competition for employment opportunities becomes more intense. To improve employment guidance and increase the employment rate and quality, it is essential to evaluate preliminary employment data, identify the primary variables that influence employment, and provide decision support for decision-makers. In the era of new media, the influx of massive information can make it difficult for undergraduates to distinguish between true and false information, making it necessary to analyze the employment situation of innovative talents using artificial intelligence. However, historical employment data is generally a dataset with high noise due to the diversity and complexity of actual employment situations. Differences in accuracy requirements among various institutions also make handling issues such as different accuracy requirements and noise adaptation abilities challenging. To address this issue, this study proposes applying a decision tree algorithm based on a multi-scale rough set model to analyze university employment data. By introducing the multiscale concept into rough set theory and drawing on variable precision rough sets, the analysis's accuracy and noise adaptation ability can be improved. Experimental results show that the proposed algorithm performs well in terms of classification accuracy and running time, and the employment situation of creative minds can be effectively analyzed. Moreover, the decision tree has a simple structure, concise rules, no inseparable datasets, and a fast running speed.