A　brain　functional　connectivity　classification　method　based　on　significant　sparse　strong　correlation　was　proposed　to　solve　problems　that　data　dimension　of　brain　functional　connectivity　is　too　high　and　many　redundant　features　affect　the　accuracy　of　neural　network　classification.　In　this　method,　the　salient　features　sparse　module　was　used　to　filter　and　enhance　the　original　features.　The　sparse　strong　correlation　feature　context　fusion　module　was　used　to　aggregate　the　salient　feature　information　in　different　receptive　fields.　The　fully　connected　neural　network　was　used　for　classification　prediction.　The　results　on　ABIDE　and　ADHD-200　datasets　showed　that　the　accuracy　of　brain　functional　connection　classification　algorithm　was　improved　by　10.41%　and　12.5%　respectively　compared　with　the　existing　brain　functional　connection　classification　algorithm.　The　visualization　results　of　the　important　features　show　that　the　proposed　method　can　accurately　locate　the　brain　regions　related　to　disease,　which　has　a　certain　practical　value.　©　2022　Zhejiang　University.　All　rights　reserved.