Schizophrenia is a severe psychotic disorder clinically characterized by positive and negative symptoms, but their neural bases remain poorly understood. Here, we utilized a nested-spectral partition (NSP) approach to detect hierarchical modules in resting-state brain functional connectivity networks in schizophrenia patients and healthy controls, and we studied the dynamic transitions of segregation and integration as well as their relationships with clinical symptoms. Schizophrenia brains showed a more stable global integrating process and a more variable segregating process, thus maintaining higher segregation, especially in the limbic system. Attention systems were associated with hallucinations, and default-mode network (DMN) and control systems were related to avolition. In a machine-learning model, NSP-based features outperformed classical graph measures at predicting positive and negative symptom scores. Multivariate analysis confirmed that positive and negative symptoms had opposite effects on dynamic segregation and integration of brain networks. Gene ontology analysis revealed that the effect of negative symptoms on brain networks was related to autistic, aggressive and violent behavior; the effect of positive symptoms was associated with hyperammonemia and acidosis; and the interaction effect was correlated with abnormal motor function. Our findings could contribute to the development of more accurate diagnostic criteria for positive and negative symptoms in schizophrenia.