To　solve　the　classification　model　overfitting　problem　caused　by　the　high　dimension　and　small　sample　properties　of　functional　magnetic　resonance　imaging　(fMRI)　data,　a　feature　selection　framework　of　whole-brain　fMRI　data　combining　L1-norm　regularization　and　L2-norm　regularization　in　softmax　regression　is　proposed.　Firstly,　the　whole　brain　is　divided　into　the　region　of　interest　(ROI)　and　the　region　of　non-interest　(RONI)　in　terms　of　the　characteristics　of　brain　cognition.　Then,　L2-norm　regularization　shrinking　the　weighting　coefficients　is　used　to　model　all　voxels　in　ROI　while　L1-norm　regularization　with　a　sparse　effect　is　employed　for　modeling　the　activated　voxels　in　RONI.　Finally,　the　regularized　softmax　regression　model　of　whole-brain　fMRI　data　is　constructed　by　integrating　all　voxels　in　ROI　and　the　activated　voxels　in　RONI.　The　experimental　results　on　Haxby　datasets　show　that　the　regularization　strategies　of　L2-norm　and　L1-norm　effectively　improve　the　whole-brain　classification　performance　compared　to　some　other　methods.　©　2016,　Science　Press.　All　right　reserved.