How brains realize learning and perception is an essential question for both artificial intelligence and neuroscience communities. Since the existing artificial neural networks (ANNs) are different from the real brain in terms of structures and computing mechanisms, they cannot be directly used to explore the mechanisms of learning and dealing with perceptual tasks in the real brain. The dendritic neuron model is a computational model to model and simulate the information processing process of neuron dendrites in the brain and is closer to biological reality than ANNs. The use of the dendritic neural network model to deal with and learn perceptual tasks plays an important role in understanding the learning process in the real brain. However, current learning models based on dendritic neural networks mainly focus on simplified dendritic models and are unable to model the entire signal-processing mechanisms of dendrites. To solve this problem, this study proposes a learning model of the biophysically detailed neural network of medium spiny neurons (MSNs). The neural network can fulfill corresponding perceptual tasks through learning. Experimental results show that the proposed model can achieve high performance on the classical image classification task. In addition, the neural network shows strong robustness under noise interference. By further analyzing the network features, this study finds that the neurons in the network after learning show stimulus selectivity, which is a classical phenomenon in neuroscience. This indicates that the proposed model is biologically plausible and implies that stimulus selectivity is an essential property of the brain in fulfilling perceptual tasks through learning.