We present a dynamic functional connectivity (DFC)-based classification analysis of functional magnetic resonance imaging (fMRI) data from veterans with a type of post-traumatic stress disorder (PTSD) and from matched normal control (NC) veterans. Whole-brain resting-state fMRI (rsfMRI) data which were scanned from 23 PTSD (mean age 49) and 30 NC (mean age 50) veterans were used for analyses. A computational method using statistics of DFC and support-vector machine (SVM) classifier were used to correctly classify PTSD vs NC with up to 98% accuracy. Results show that, SVM-based machine learning technique, combined with simple t-test method for feature extraction utilizing the standard deviation of DFC, performed significantly better than a convolutional neural network (CNN) based deep learning method in terms of classification accuracy, with around 98% accuracy for the former vs. around 60% accuracy for the latter; it also outperformed static functional connectivity-based classification, which resulted in only up to 72% accuracy.