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[目的/意义]从知识主体吸收知识的认知视角出发,研究科研创新网络中知识动态增长的演化规律以及影响机制,引入复杂网络理论,构建基于科研创新网络的知识增长过程模型。[方法/过程]考虑科研创新网络中知识主体获取知识的空间结构和认知特征,基于对数透视法则将网络中实际的知识物理分布转换为知识主体自身的认知空间;在分析知识吸收过程中相关影响因素的基础上,深入理解知识动态增长的流动模式构建知识增长函数,并从效率和公平相匹配角度界定知识增长的绩效。[结果/结论]科研创新网络中的知识增长绩效受网络结构随机化程度的影响呈现先下降后上升的趋势;知识主体具备适度的知识传播能力和吸收能力才能实现网络中最优的知识累积效应;主体获取知识的认知过程受对数法则的约束,且随着其认知半径的增大,网络的知识增长绩效越高。
[Purpose / Significance] From the cognitive perspective of knowledge-based knowledge absorption, this paper studies the evolution law and mechanism of knowledge dynamic growth in scientific research innovation network, introduces complex network theory and builds a knowledge growth process model based on research innovation network. [Method / Process] Considering the spatial structure and cognitive characteristics of knowledge subjects in scientific research and innovation network, the actual distribution of knowledge and physics in the network is converted into the cognitive space of knowledge body based on logarithmic perspective law. When analyzing the process of knowledge absorption Based on the relevant factors, we should further understand the dynamic mode of knowledge growth and build a knowledge growth function, and define the performance of knowledge growth from the angle of efficiency and fairness. [Results and Conclusions] The knowledge growth performance of the research innovation network is influenced by the degree of randomization of the network structure first and then increases. The main body of knowledge has the appropriate knowledge transmission ability and absorptive capacity to achieve the optimal knowledge accumulation effect in the network . The subject’s cognitive process of acquiring knowledge is constrained by the logarithm law, and as its cognitive radius increases, the network’s knowledge growth performance is higher.