基于AHP-KNN算法的农业人才培养模型构建与应用

IF 1.2 Q2 MATHEMATICS, APPLIED
Shubing Qiu, Yong Liu, Xiaohong Zhou
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引用次数: 0

摘要

目前,中国农业人才缺口不断扩大,大部分企业缺乏农业核心人才,对社会经济造成了很大影响。针对这一问题,将层次分析法(AHP)与优化的k近邻算法相结合,提出了一种改进的AHP- knn算法,并在此基础上提出了一个农业人才培养模型。结果表明,改进的AHP-KNN算法的分类准确率为96.2%,分类时间为27.5 s,均优于对比算法。结果表明,该算法可以提高农业人才的分类精度。因此,可以利用该模型将具有相同特征的农业人才分类为一类,进行针对性的培养,高效、快速地培养出全方位的农业人才,从而改善目前农业人才严重短缺的现状。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Construction and Application of Agricultural Talent Training Model Based on AHP-KNN Algorithm
At present, the gap of agricultural talents in China is continuously widening, and most enterprises lack agricultural core talents, which has caused great impact on the social economy. To solve this problem, an improved AHP-KNN algorithm is proposed by combining the analytic hierarchy process (AHP) and the optimized K-nearest neighbor algorithm, and an agricultural talent training model is proposed based on this algorithm. The results show that the classification accuracy and classification time of the improved AHP-KNN algorithm are 96.2% and 27.5 seconds, respectively, both of which are superior to the comparison algorithm. The result shows that the classification accuracy of agricultural talents can be improved by using this algorithm. Therefore, the model can be used to classify agricultural talents with the same characteristics into one class, carry out targeted training, and train all-round agricultural talents efficiently and quickly, so as to improve the serious shortage of agricultural talents at present.
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来源期刊
Journal of Applied Mathematics
Journal of Applied Mathematics MATHEMATICS, APPLIED-
CiteScore
2.70
自引率
0.00%
发文量
58
审稿时长
3.2 months
期刊介绍: Journal of Applied Mathematics is a refereed journal devoted to the publication of original research papers and review articles in all areas of applied, computational, and industrial mathematics.
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