Schizophrenia is associated with cognitive impairments. Exercise interventions including yoga and aerobic exercise have been shown to improve cognitive functioning in schizophrenic patients. Yet the underlying neural basis is not clear for this cognitive improvement. This work aimed to investigate the brain functional effect caused by exercise interventon in female patients with early psychosis. Resting-state fMRI was collected longitudinally from 71 patients who were randomized into three programs: yoga, aerobic exercise and waitlist control. Functional connectivity matrices of each individual at baseline and 12-week follow-up timepoint were estimated. Then the connectivity changes were calculated and used as potential predictors to classify the three groups. A machine learning method gcForest was used to train classification models on a subset and tested on the rest of the data. Classification performance was evaluated using multiple n-fold cross-validation to ensure a robust estimate of the accuracy. The classification accuracy ranges from 86.31 to 94.00. The most predictive features were examined in the brain, which include connectivity changes along several major pathways in high order functional networks including default mode and executive control networks. This is the first study showing that the connectivity alterations can successfully distinguish intervention from control groups, and also detect the two different types of intervention: yoga and aerobic exercise. Findings suggest that the altered functional connectivity may contribute to the cognitive improvement after intervention. Our work sheds light on the use of advanced neuroimaging and machine learning approaches to explore potential biomarkers for predicting outcomes of exercise intervention in psychosis.