Stroke is one of the main causes of disability in human beings, and when the occipital lobe is affected, this leads to partial vision loss (homonymous hemianopia). To understand brain mechanisms of vision loss and recovery, graph theory-based brain functional connectivity network (FCN) analysis was recently introduced. However, few brain network studies exist that have studied if the strength of the damaged FCN can predict the extent of functional impairment. We now characterized the brain FCN using deep neural network analysis to describe multiscale brain networks and explore their corresponding physiological patterns. In a group of 24 patients and 24 controls, Bi-directional long short-term memory (Bi-LSTM) was evaluated to reveal the cortical network pattern learning efficiency compared with other traditional algorithms. Bi-LSTM achieved the best balanced-overall accuracy of 73% with sensitivity of 70% and specificity and 75% in the low alpha band. This demonstrates that bi-directional learning can capture the brain network feature representation of both hemispheres. It shows that brain damage leads to reorganized FCN patterns with a greater number of functional connections of intermediate density in the high alpha band. Future studies should explore how this understanding of brain FCN can be used for clinical diagnostics and rehabilitation.