机器学习视角下中国去产能产业的识别  

Identification of China’s Overcapacity Industries From Machine Learning Perspective

作　　者：李文超[1] 关荣迪 贺丹[1] Li Wenchao;Guan Rongdi;He Dan(School of Finance and Economics,Jiangsu University,Zhenjiang Jiangsu 212013,China)

机构地区：[1]江苏大学财经学院,江苏镇江212013

出　　处：《统计与决策》2020年第6期87-90,共4页Statistics & Decision

基　　金：国家自然科学基金资助项目(71704067,71974078);教育部人文社会科学基金资助项目(17YJC790080)。

摘　　要：随着中国环境压力的增加,政府提出了供给侧改革,去产能是改革的主要内容,但是由于产业特征的实时演变,需要对政策进行完善。文章运用模糊C均值算法和支持向量机算法分析现阶段需要进行去产能的产业,结果发现在现行去产能政策中大部分行业是需要去产能的,但煤炭开采和洗选业以及铁路、船舶、航空航天和其他运输设备制造业已不适合继续去产能,同时将化学原料和化学制品制造业加入去产能行列中。With the increase of environmental pressure in China,the government has put forward supply-side reform,and cutting overcapacity is a major part of the reform,but due to the real-time evolution of industrial characteristics,the policy needs to be improved.This paper uses the fuzzy C-means algorithm and support vector machine(SVM)algorithm to analyze the industries that need to cut overcapacity.The result shows that most industries need to cut capacity in the current policy of cutting capacity,but coal mining and coal washing industry,as well as the manufacturing of railways,ships,aerospace and other transport equipment,are no longer suitable for continued capacity cuts,and meanwhile,the manufacturing of chemical raw materials and products should be included in the industries of cutting capacity.

关 键 词：机器学习 模糊C聚类 支持向量机 去产能产业 

分 类 号：F421[经济管理—产业经济]