Abstract: To explore the functional brain imaging markers of epileptic seizure side, a joint scheme of functional connectivity specificity modeling and supervised machine learning with resting-state functional magnetic resonance is proposed. Twenty temporal lobe epilepsy patients with structural images suggestive of the seizure side (equally divided into left and right groups) and 142 healthy individuals were selected. We used healthy individuals as reference, and a functional connectivity specificity model was constructed to score the functional connectivity of each brain region for each patient. The significance of the difference in scoring values between the left and right groups was statistically analyzed to obtain the landmark brain regions that were sensitive to the seizure side. The scoring values were used as a feature vector inputted into a probabilistic neural network to achieve the fixation of the side and cross validation was used. The results show that: functional imaging markers sensitive to the ictal side are in six brain regions, including the amygdala and paracentral lobule, with a classification accuracy of 90.0%, which is higher than the current level of machine learning-assisted epilepsy research.