Objective On the basis of diffusion tensor imaging (DTI), we analyzed the properties of structural brain network by selecting the characteristics of structural network which were higher correlated with cognitive performance to estimate the prediction models. Based on the estimated models, the score of cognitive performance of subjects could be evaluated objectively according to their magnetic resonance imaging (MRI). Methods We used diffusion tensor imaging to construct brain structural network and connection matrix for 94 healthy elders. In order to obtain the characteristics of structural brain network, we analyzed connection matrices by using graph theory and diffusion tractography. We selected significant features by the correlation analysis between the characteristics of brain network and subject’s mini-mental state examination (MMSE) score. These features would then be used to estimate the models based on five kinds of machine learning algorithm respectively to predict the cognitive performance. Finally, the performance of the five prediction models would be analyzed and discussed. Results Under the condition of the correlation coefficient greater than 0.22 and P value less than 0.05, thirty characteristics of brain network were selected as features and the related anatomical regions located in 12 brain areas according to the automated anatomical labeling (AAL) template. Among the 5 algorithms, Gaussian processes model was more accurate due to the higher correlation coefficient of 0.89 and the lower mean absolute error of 2.01. Conclusions We successfully established prediction model based on brain structural network metrics derived from DTI which could be employed to effectively predict subject’s cognitive performance score.