随着信息技术的快速发展和社会化决策的兴起，组织的管理和决策模式正在发生深刻的变革，决策问题中涉及的方案规模和偏好信息数量显著增加。传统的多准则分类(MCS)方法由于处理机制和实现方式的限制，已经难以处理实际的决策问题。本项目重点研究了具有大规模方案集和偏好信息的MCS问题，利用MapReduce分析框架，提出了不完全偏好信息下基于MapReduce的MCS方法和案例分类信息下基于MapReduce的MCS方法。针对每一类问题，分别采用不同的准则集成模型，探究大规模方案集和偏好信息的并行处理方式以及偏好信息中存在高度不一致性的解决方法。最后通过仿真实验和统计分析，对所提方法的稳健性进行检验。研究成果将为解决实际的MCS问题提供理论和方法上的支持。

With the development of information technology and social decision-making, there is a profound change in the management and decision-making in organizations, and the scale of alternatives and preference information in decision-making problems increases significantly. Due to the limitation of mechanism and implementation, traditional multiple criteria sorting (MCS) approaches cannot address practical decision-making problems. This research studies MCS problems with large-scale sets of alternatives and preference information. It utilizes the MapReduce framework and proposes MCS approaches based on the MapReduce framework with incomplete preference information and those with assignment examples. Moreover, it employs different preference models and explores the manner of handling large-scale sets of alternatives and preference information in parallel and the method to resolve inconsistencies in preference information. The robustness of proposed MCS approaches is validated through simulation experiments and statistical analysis. This research will provide theoretical and methodological support for addressing practical MCS problems.