[目的/意义]探究算法民间理论如何在多主体互动中实现由分散认知向群体共识的演化路径，为人们理解算法民间理论的形成过程及演化机制提供有益启示。[方法/过程]从知识扩散视角出发，采用ABM建模仿真方法，将信息用户抽象为具有异质性的认知主体，并结合香农熵、结构熵与LMC复杂度等指标，探究算法民间理论的演化过程以及算法知识传播扩散与分布状态。[结果/结论]算法民间理论的演化过程具有萌芽、发展、平稳、衰退四个演化阶段；传播网络由碎片化向整合化演进，知识分布呈现出共识、均衡与极化三种演化形态；个体认知能力与基础知识水平是影响民间理论演化的关键因素，并且差异过大可能引发知识鸿沟与认知极化。

[Purpose/significance] To explore how algorithmic folk theories evolve from dispersed individual perceptions to collective consensus through multi-agent interactions, thereby providing insights into the formation process and evolutionary mechanisms of algorithmic folk theories. [Method/process] From the perspective of knowledge diffusion, this study employs an Agent-Based Modeling (ABM) simulation approach, conceptualizing information users as heterogeneous cognitive agents. By incorporating metrics such as Shannon entropy, structural entropy, and LMC complexity, it investigates the evolutionary process of algorithmic folk theories as well as the diffusion and distribution patterns of algorithmic knowledge. [Result/conclusion] The evolution of algorithmic folk theories undergoes four stages:emergence, development, stabilization, and decline. The diffusion network evolves from fragmentation to integration,while knowledge distribution exhibits three patterns: consensus, equilibrium, and polarization. Individual cognitive ability and foundational knowledge level are identified as key factors influencing the evolution of folk theories, and excessive disparities may lead to knowledge gaps and cognitive polarization.