Discriminant　characteristics　of　brain　functional　connectivity　can　be　used　as　a　biomarker　for　the　diagnosis　of　neuropsychiatric　diseases.　Using　a　machine　learning　method　to　identify　is　an　important　topic　in　brain　science　research.　Most　of　the　existing　recognition　methods　of　brain　functional　connectivity　biomarkers　ignore　the　impact　of　the　characteristics　of　high-dimensional,　continuous　and　multi　noise　of　brain　functional　connectivity　data　on　the　recognition　performance,　resulting　in　the　weak　classification　ability　of　the　obtained　biomarkers.　This　paper　proposes　a　brain　function　connectivity　biomarker　recognition　method　based　on　neighborhood　decision　rough　sets.　Firstly,　according　to　the　characteristics　of　continuity　and　high　noise　of　brain　function　connectivity　data,　a　neighborhood　decision　rough　set,　which　can　effectively　deal　with　continuous　and　high　noise　data,　is　introduced　to　identify　brain　functional　connectivity　discriminant　features　with　stronger　classification　ability　as　biomarkers.　Then,　according　to　the　high-dimensional　characteristics　of　brain　function　connectivity　data,　the　efficiency　of　identifying　brain　function　connectivity　biomarkers　by　the　neighborhood　decision　rough　set　is　guaranteed　by　quickly　generating　the　neighborhood　and　reducing　the　feature　search　space.　The　experimental　results　on　the　ABIDE　I　and　ADNI　data　sets　show　that　the　proposed　method　can　accurately　and　quickly　obtain　the　discriminant　features　of　brain　functional　connectivity　with　stronger　classification　ability,　and　is　expected　to　provide　more　accurate　biomarkers　for　the　diagnosis　of　neuropsychiatric　diseases.　©　2023　Northeast　University.　All　rights　reserved.