利用改进的新陈代谢GM（1，1）模型，借助MATLAB对江苏省高技术产业2016-2020年的人才总量进行灰色预测，并与通过模型预测出的广东、浙江的高技术产业的人才总量进行对比。建模结果表明，改进的新陈代谢GM（1，1）模型的预测精度比常规模型提高了将近50%，也比新陈代谢GM（1，1）模型和背景值优化模型精度高。预测结果表明，“十三五”末江苏省高技术产业人才总量约为2549424人，位于广东之后；人才年均增速约为0.5%，位于浙江、广东之后。

Using the improved metabolic GM (1,1) model, the paper forecasted the total talent of Jiangsu high-tech industry from 2016 to 2020 by means of MATLAB, and compared with the total talent of high-tech industry in Guangdong and Zhejiang which were predicted by model. The modeling results show that the improved metabolic GM (1,1) model has a better prediction accuracy than the conventional model by nearly 50%, and is more accurate than the metabolic GM (1,1) model and the background value optimization model. Tthe forecast results show that, to the end of "thirteen five plan", high-tech industry demand for talent in Jiangsu will reach 2549424, which is ranked behind Guangdong, and the average annual growth rate will amount to 0.5%, which is ranked behind Zhejiang and Guangdong.

江苏省软科学研究项目“江苏战略性新兴产业国际顶尖科研人才分布研究”（项目编号：BR2017031）；江苏省社科应用研究精品工程项目“江苏战略性新兴产业人才需求预测与开发研究”（项目编号：17SRB-15）