Intelligent analysis system of college students' employment and entrepreneurship situation: Big data and artificial intelligence-driven approach

Abstract

This study proposes an analysis plan for college students' employment and entrepreneurship based on big data and artificial intelligence, aiming to provide an important basis for the development of employment guidance activities. To realize feature analysis and useful information extraction of large-scale text information data, this study adopts a big data mining algorithm based on fuzzy hierarchical clustering analysis and semantic similarity correlation feature extraction. The algorithm uses generalization mapping to construct a semantic concept tree, and a fuzzy analytic hierarchy process to judge the semantic similarity and relevance of big data. This study demonstrates the efficacy of a big data mining algorithm through experimental verification. Two public datasets were used to validate the proposed method. The experimental results revealed that the method could efficiently process and analyze large textual datasets, leading to superior data classification performance.