Functional magnetic resonance imaging (fMRI) can measure changes in blood oxygenation level-dependent (BOLD) in the human brain caused by some stimuli or tasks, which helps us understand human brain mechanisms and functions. Recent studies demonstrate that there are both shared and specific neural representations between nature and drawing images. However, there is a lack of more detailed studies on the differences at the various levels of image abstraction. In this paper, we proposed to recognize concept levels from the image abstractions, including photographs, drawings, and sketches. More specifically, this study first conducts preprocessing processes to mitigate fMRI noise, such as respiratory and head movement, and then constructs the functional connectivity matrix based on the fMRI segmentation corresponding to different stimuli, leading to our used datasets. On the resulting dataset, we trained several data classifiers to obtain the mapping from fMRIs to the three types of stimuli. In addition, we discussed experimental parameters to check their impacts on classification performance. The evaluated results show that different concept-level images could be recognized at an effective accuracy, where the deep learning model achieves the best performance. This study contributes to an understanding of the abstraction level of concept formulation in the brain. The results can help treat brain disorders and make learning plans.