To　improve　the　3D　spatial　feature　extraction　ability　of　current　brain　disease　classification　methods,　a　novel　classification　method　based　on　3D　attention　convolutions　and　self-supervised　learning　was　proposed.　First,　a　3D　attention　convolutional　neural　network　based　on　residual　connections　was　proposed　to　extract　spatial　features,　aiming　to　distinguish　the　importance　of　different　spatial　positions　in　voxel　data　using　3D　attention　mechanism.　Second,　a　multi-task　learning　framework　based　on　self-supervised　learning　was　constructed　by　using　the　learned　spatial　features,　and　a　self-supervised　auxiliary　task　based　on　spatial　continuity　was　used　to　further　mine　the　spatial　dependence　of　voxels.　Finally,　the　model　parameters　were　optimized　by　jointly　training　the　target　classification　task　and　self-supervised　auxiliary　task,　and　then　the　classification　performance　can　be　largely　boosted.　Experimental　results　on　ABIDE-I　and　ABIDE-II　show　that　the　proposed　method　has　superior　classification　performance　and　the　results　also　demonstrate　good　interpretability.　©　2024　Beijing　University　of　Technology.　All　rights　reserved.