A method for evaluating college students’ career suitability based on fuzzy clustering algorithm

Abstract

Background

With the growing demand for high-quality talents in society, college students often lack effective guidance when facing career choices. Traditional career suitability assessment methods focus on a single dimension, such as interest or ability assessment, which is difficult to fully reflect students’ career inclinations and potential. Therefore, combining multi-dimensional analysis methods, especially fuzzy clustering algorithms, can more comprehensively evaluate students’ career suitability.

Objective

This study aims to evaluate the career suitability of college students through fuzzy clustering algorithm and provide personalized advice for career planning.

Methods

In the process of the study, multi-dimensional data of 500 college students were first collected, including career interests, academic performance, ability assessment, and personality traits. Holland’s career interest test, academic performance data, ability assessment, and Big Five Personality Theory were used to comprehensively evaluate the students. Subsequently, the fuzzy C-means (FCM) algorithm was used to cluster the data to optimize the number of clusters and the fuzziness index, and finally the students’ career suitability scores were obtained.

Results

The experimental results show that the fuzzy clustering algorithm shows high accuracy and good clustering effect when processing multi-dimensional data. Compared with the traditional algorithm, it has achieved better results in multiple indicators such as accuracy, F1 score, and AUC. The conclusion of this study shows that the fuzzy clustering algorithm can effectively deal with the multi-dimensional uncertainty in career suitability assessment and provide a more personalized and accurate career recommendation plan.

Conclusions

This method not only helps students make more scientific career choices but also provides new ideas for educational institutions and career consulting platforms. The research results have important practical application significance and can provide theoretical support and data reference for students’ career planning, education policy formulation, and related fields.