Abstract： Neuropsychiatric diseases seriously affect the anatomical structure of the brain,nervous system function,and mental health of patients.Early identification and diagnosis are of great significance for the treatment and rehabilitation of patients with neuro-psychiatric diseases.The construction of complex brain networks based on neuroimage data can be used to quantitatively analyze the brain structure and function abnormalities in patients with neuropsychiatric diseases,and provide an important reference for the devel-opment of neuroimaging biomarkers for neuropsychiatric diseases.In recent years,graph neural network has been widely used in the di-agnosis of neuropsychiatric diseases because of their advantages of processing non-Euclidean data and making full use of the topological structure and attributes of nodes and connected edges.We summarize the basic principles of graph convolutional network and the latest research progress in neuropsychiatric diseases,and look forward to research hotspots such as dynamic brain network,large sample and multi center,visualization and interpretability.