Construction of Big Data Analysis Model for High School Students' Career Planning Interest Events Based on Weight Algorithm
- DOI
- 10.2991/978-94-6463-108-1_60How to use a DOI?
- Keywords
- Career Planning; Interest; Weight Algorithm; Big Data Analysis Model
- Abstract
Interest is one of the most important factors in senior high school students' career planning, which is related to the accuracy of senior high school students' career planning conclusions. At present, Holland Interest Assessment is the most commonly used tool for senior high school students' interests. However, senior high school students are often difficult to devote themselves to the assessment, which often leads to some deviation in the results. Based on this, this paper uses the principle of Xiangyang's career interest event analysis method for reference, introduces the structure of weight algorithm and big data analysis model, and constructs a big data analysis model of high school students' career planning interest events, which provides an effective reference for analyzing interest factors in the process of high school students' career planning.
- Copyright
- © 2022 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Xin Zhao AU - Lunjun Cheng PY - 2022 DA - 2022/12/30 TI - Construction of Big Data Analysis Model for High School Students' Career Planning Interest Events Based on Weight Algorithm BT - Proceedings of the 2022 International Conference on Computer Science, Information Engineering and Digital Economy (CSIEDE 2022) PB - Atlantis Press SP - 526 EP - 532 SN - 2352-538X UR - https://doi.org/10.2991/978-94-6463-108-1_60 DO - 10.2991/978-94-6463-108-1_60 ID - Zhao2022 ER -