Research On Cultivating the Employment Concept of College Students with The Great Spirit of Building the Party - Based on A Data-Driven Approach

Abstract

One of the most essential ways to see the worth of college students is via employment, which is also the main focus of student development programs for the Spirit of Building the Party. Some real-world issues with the conventional approach to college career counselling stem from the disconnection between students' education and the real world. For example, there is often a misalignment between students' social needs and their comprehensive skill sets and between students' job-seeking needs and their actual employment situations. There has to be extensive research into how to better assist college graduates in finding work that suits their skills and experience so that society can fulfil the practical demands of its growth in this area for the Spirit of Building the Party. Problems in the workplace have garnered a lot of attention because they threaten society's steady progress and the quality of life for its citizens. The nation now needs to tackle the issue of challenging employment for the Spirit of Building the Party. Institutions of higher learning have responded to societal and economic demands by emphasizing the development of students' social abilities and increasing their access to the job market. To provide a solid groundwork for creating employment assistance programs, this research suggests an analytical strategy for college students' jobs based on a data-driven approach for the Spirit of Building the Party. The proposed model is used to plan the supply and structure of skills from a talent supply standpoint. In contrast, the regression model forecasts university students' employment requirements for the Spirit of Building the Party. The data mining technique used in this work combines enhanced fuzzy hierarchical clustering and feature extraction based on semantic similarity correlation. The testing findings showed that the method performed better in categorizing data and could easily handle and analyze big-text datasets