Visualization research of college students' career planning paths integrating deep learning and big data

• Main Authors: Guo, Jing, Qi, Lei
• Format: Article
• Published: Hindawi Ltd 2022
• Subjects: QA Mathematics; TA Engineering (General). Civil engineering (General)
• Online Access: http://eprints.um.edu.my/42287/

As China's education enters a high-level stage, more and more students graduate from Chinese colleges and universities. In particular, the current employment environment is flexible and multilateral, and there are more and more opportunities to choose from. In view of this situation, this article aims to visualize the career planning (CP) path of college students, so as to help college students adapt to the environment of flexible employment. For deep learning and big data (DLBA) technology, this article proposes the LSTM-Canopy algorithm, which is added to the traditional Canopy algorithm to enhance the self-learning clustering ability of the algorithm. Also, this study applies this algorithm to the visualization system of college students' CP path, which can effectively improve the analysis and judgment of experts on career. The experiments in this article have proved that the system can meet the normal use of 400-500 users, and the system server has successfully passed 40 load tests, and the running time is also less than 2.5s, which proves the reliability of the system.