Abstract: In order to improve the quality management and enhance the efficiency of decision-making in machinery manufacturing industry, this paper took the typical machinery enterprise’s business as an example and explored the data of management information by Cross-Industry Standard Process for Data Mining (CRISP-DM) standard process. By using the gain ratio calculation method based on ID3 decision tree algorithm, the study generated a decision tree model and made it an initial implementation in the company’s Enterprise Resources Planning (ERP) system. By test and analysis, the result shows that the model can standardize the business process and enhance the decision-making efficiency.

Key words: machinery industry, Cross-Industry Standard Process for Data Mining (CRISP-DM), decision tree, ID3 algorithm, data mining, product quality management

摘要： 针对机械制造业中质量管理不规范、决策效率偏低问题，以典型的机械制造企业为切入点，运用ID3决策树算法，以数据挖掘跨行业标准过程（CRISP-DM）对其质量管理信息进行数据挖掘。利用基于信息增益率的计算分类技术，生成了决策树模型，并将该模型在企业资源计划（ERP）中进行了初步实现。通过测试分析，该模型能有效提高管理决策效率，规范处理流程。

关键词: 机械制造业, 数据挖掘跨行业标准过程, 决策树, ID3算法, 数据挖掘, 产品质量管理