随着大数据时代的到来，从大数据中获取的知识已经成为企业创新所需知识的重要组成部分。大数据知识的复杂特性使得大数据环境下的知识转移活动更加复杂，企业进行知识转移决策面临诸多新的挑战。本项目针对大数据环境下知识转移影响因素的变化特征和系统结构特点，从理论与实证两个方面开展促进大数据环境下知识转移的优化方法与策略研究。首先，在对大数据环境下知识转移活动的系统分析基础上，找出大数据环境下知识转移决策的关键问题及影响因素作用机理；然后,针对大数据环境下转移知识结构配置、知识转移主体遴选和知识转移时间优化三个关键问题进行深入分析，构建大数据环境下知识转移优化方法与模型；并对模型进行模拟仿真与实证验证；最后，给出促进知识转移的优化策略。本项目研究成果将进一步丰富与完善企业知识转移的相关理论与方法，也为我国大数据知识服务平台的构建与完善提供策略建议。

With the advent of the big data era, the knowledge from big data has become an important part of knowledge that enterprises need for innovation. In the big data environment, the activities of knowledge transfer are more complicated because of the complex characteristics of the big data knowledge, and enterprises will face many new challenges when transferring knowledge. Considering the changes of the impact factors and the system structure of knowledge transfer in the big data environment, the research on the optimization methods and strategies of promoting knowledge transfer between organizations in the big data environment is conducted by theoretical and empirical methods. Firstly, based on the systematic analysis of the knowledge transfer in the big data environment, the key decision-making problems and the impact factors of knowledge transfer in the big data environment can be identified. Then, according to the research on three key decision making problems of the allocation of knowledge structure, the selection of knowledge transfer organizations and the time optimization of knowledge transfer, the optimization methods of knowledge transfer in the big data environment are proposed, and the models are constructed. Furthermore, the models are simulated and verified by empirical data. Finally, the optimization strategies of promoting knowledge transfer are presented. Research achievements will enrich the theories and methods of knowledge transfer, and provide strategic guidance for the construction and improvement of the big data platform in China.