[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] In order to research the evolution rule and the influence mechanism of knowledge dynamic growth in the scientific innovation network from the cognitive perspective of absorbing knowledge, this paper presents a process model of knowledge growth by introducing the complex network theory. [Method/process] Firstly, considering the spatial structure and cognitive features of the knowledge agent to receive knowledge, this paper transformed the actual physical distribution of network knowledge to the cognitive space of knowledge agent based on the law of logarithmic perspective. Secondly, on the basis of analyzing related influence factors and the flow pattern of knowledge absorptive process, the knowledge growth function was constructed. Thirdly, the knowledge growth performance could be calculated from the perspective of efficiency and justice. [Result/conclusion] Knowledge growth performance is decreased first and then increased with increasing structural randomization. When agents own moderate knowledge diffusion and absorptive capacity, knwoledge cumulative effect can achieve an optimal level. Meanwhile, the cognitive process of agents to receive knowledge is restrained by the logarithmic law, and the growth performance will increase with the increasing congnitive radius.
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