The evaluation and selection of green suppliers are some of the most important tasks in green supply chain management (GSCM). The purpose of this study is to develop an effective green supplier evaluation model. Currently the evaluation criteria are determined through a literature review combined with decision-maker opinions. A few studies have used data mining techniques to screen the core criteria. This study proposes a novel hybrid MCDM model, that integrates the support vector machine (SVM), the fuzzy best worst method (FBWM) and, the fuzzy technique for order preference by similarity to an ideal solution (FTOPSIS) approaches to select the most suitable green suppliers. A case study, using data from a multinational electronics manufacturer, is carried out for illustration. First, the SVM is used to extract the core criteria from the historical data. The original 25 criteria are reduced to 13 criteria. Then, the FBWM is used to obtain the weights of the core criteria. Finally, the FTOPSIS is used to integrate the performance and prioritize the green suppliers. Finally, practical management implications and suggestions for improvement for decision-makers and green suppliers are provided

绿色供应商的评估和选择是绿色供应链管理（GSCM）中一些最重要的任务。本研究的目的是开发一种有效的绿色供应商评估模型。目前评价标准是通过文献综述结合决策者意见来确定的。一些研究使用数据挖掘技术来筛选核心标准。本研究提出了一种新的混合 MCDM 模型，该模型集成了支持向量机 (SVM)、模糊最佳最坏方法 (FBWM) 和通过与理想解决方案相似的顺序偏好模糊技术 (FTOPSIS) 方法来选择最合适的绿色供应商。使用跨国电子制造商的数据进行案例研究以进行说明。第一的，SVM 用于从历史数据中提取核心标准。原来的 25 个标准减少到 13 个标准。然后，FBWM 用于获得核心标准的权重。最后，FTOPSIS 用于整合绩效并优先考虑绿色供应商。最后，为决策者和绿色供应商提供了实际的管理启示和改进建议