Employment Quality Evaluation Model Based on Hybrid Intelligent Algorithm

被引:2
作者
Gu, Xianhui [1 ]
Wang, Xiaokan [1 ]
Liang, Shuang [2 ]
机构
[1] Henan Mech Elect Vocat Coll, Xinzheng 451191, Hennan, Peoples R China
[2] Univ Florence, I-50041 Florence, Italy
来源
CMC-COMPUTERS MATERIALS & CONTINUA | 2023年 / 74卷 / 01期
关键词
Employment quality; fuzzy c-means clustering algorithm; grey correlation analysis method; evaluation model; index system; comparative test; GREY RELATIONAL ANALYSIS; COMPREHENSIVE EVALUATION; NEURAL-NETWORK; OPTIMIZATION;
D O I
10.32604/cmc.2023.028756
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In order to solve the defect of large error in current employment quality evaluation, an employment quality evaluation model based on grey correlation degree method and fuzzy C-means (FCM) is proposed. Firstly, it analyzes the related research work of employment quality evaluation, establishes the employment quality evaluation index system, collects the index data, and normalizes the index data; Then, the weight value of employment quality evaluation index is determined by Grey relational analysis method, and some unimportant indexes are removed; Finally, the employment quality evaluation model is established by using fuzzy cluster analysis algorithm, and compared with other employment quality evaluation models. The test results show that the employment quality evaluation accuracy of the design model exceeds 93%, the employment quality evaluation error can meet the requirements of practical application, and the employment quality evaluation effect is much better than the comparison model. The comparison test verifies the superiority of the model.
引用
收藏
页码:131 / 139
页数:9
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