Abstract: With the rapid development of global informationization, data mining and knowledge discovery of high-dimensional data have been a hotspot in the field of artificial intelligence and data science. However, the sparse sample and redundant feature issues of high-dimensional data make it challenging to ensure the generalization and interpretability of traditional statistical models and machine learning methods. Hence, this paper presents fuzzy-based concept-cognitive learning with robustness for the imbalance problem between high-dimensional data and weak knowledge evolution ability. The main idea is to explore the knowledge structure and cognitive learning mechanism of high-dimensional data from the concept perspective. This paper proposes a high-dimensional data classification method based on the concept-cognitive learning mechanism in the fuzzy formal context. Furthermore, the cognitive learning process of fuzzy concepts is described from two different perspectives by the positive and negative cognitive learning operators of fuzzy three-way concepts. Finally, the fusion of fuzzy three-way concepts completes the task of concept identification and data classification. Extensive experiments performed on 12 real data sets compared with 12 state-of-the-art classification methods also verify the robustness and effectiveness of the proposed method. The considered framework can provide a convenient novel tool for researching high-dimensional data knowledge discovery and fuzzy-based concept-cognitive learning.