Research Article
Design of Human Resource Big Data Parallel Classification System Based on Social Information Cognitive Model
@INPROCEEDINGS{10.1007/978-3-030-94551-0_36, author={Bo Sun and Cai-ming Zhang}, title={Design of Human Resource Big Data Parallel Classification System Based on Social Information Cognitive Model}, proceedings={Advanced Hybrid Information Processing. 5th EAI International Conference, ADHIP 2021, Virtual Event, October 22-24, 2021, Proceedings, Part I}, proceedings_a={ADHIP}, year={2022}, month={1}, keywords={Social information cognitive model Human resources Resource big data Parallel classification}, doi={10.1007/978-3-030-94551-0_36} }- Bo Sun
Cai-ming Zhang
Year: 2022
Design of Human Resource Big Data Parallel Classification System Based on Social Information Cognitive Model
ADHIP
Springer
DOI: 10.1007/978-3-030-94551-0_36
Abstract
In order to improve the classification function and operation performance of human resources, a new big data parallel classification system is designed. The parallel processor is installed to optimize the analog-to-digital converter, human resource data storage and wireless communication network. This paper constructs a social information cognitive model, under which human resource data can be obtained in real time, and preprocessed by data cleaning, Chinese word segmentation and stop word elimination. Human resource data features are extracted, and the similarity between the extracted data features and standard data features is calculated to realize the parallel classification function of human resource big data. Through the system test experiment, the conclusion is drawn: compared with the traditional classification system, the recall rate and accuracy rate of the design system are increased by 5.5% and 3.5% respectively, and it has more advantages in classification speed.
