With the vigorous development of the information technology industry, the information data available to mankind has shown an explosive growth trend. Dynamic concept learning is an approach that can effectively process the acquired massive data and extract valuable information from them. Concept cognitive learning (CCL) is a very active research direction in the field of dynamic concept learning, while partial order formal structure analysis (POFSA) is a concrete and practical model of CCL. However, the existing CCL algorithms in POFSA face some challenges when processing constantly changing data. Therefore, this paper is devoted to explore an incremental CCL algorithm based on three-way object partial order structure diagram (OPOSD) in POFSA with the incorporation of the thoughts of incremental learning. The features of five object categories are considered, and their incremental influences on three-way OPOSD are analyzed and their incremental CCL algorithms in three-way OPOSD are established. Based on some real famous formal contexts, this paper conducts numerical experiments, and the results show that the incremental CCL algorithm based on three-way OPOSD is consistent with human cognitive principles, and can improve the CCL performance of POFSA as well.

随着信息技术产业的蓬勃发展，人类可获得的信息数据呈爆炸性增长趋势。动态概念学习是一种可以有效处理获取​​的海量数据并从中提取有价值的信息的方法。概念认知学习（CCL）是动态概念学习领域中非常活跃的研究方向，而偏序形式结构分析（POFSA）是CCL的具体实用模型。但是，POFSA中现有的CCL算法在处理不断变化的数据时面临一些挑战。因此，本文致力于结合增量学习的思想，探索基于POFSA的三向对象偏序结构图（OPOSD）的增量CCL算法。考虑了五个对象类别的特征，分析了它们对三向OPOSD的增量影响，建立了三向OPOSD的增量CCL算法。基于一些真实的著名形式上下文，本文进行了数值实验，结果表明基于三向OPOSD的增量CCL算法与人类认知原理相一致，也可以提高POFSA的CCL性能。