In　recent　years,　semi-supervised　learning　(SSL)　for　functional　brain　network　(FBN)　classification　has　gained　considerable　attention　due　to　its　potential　to　leverage　large　amounts　of　unlabeled　data　from　multisite.　However,　existing　SSL　methods　often　struggle　to　address　the　distributional　differences　across　different　sites,　which　limits　their　ability　to　extract　discriminative　features　from　the　unlabeled　data,　thus　hindering　classification　performance.　To　overcome　this　challenge,　we　propose　a　novel　consistency　semi-supervised　FBN　classification　framework　with　prototypical-adversarial　learning,　termed　CSBNC-PAL.　Specifically,　we　first　design　a　contrastive　consistency　module　(CCM)　that　utilizes　contrastive　learning　to　more　effectively　exploit　unlabeled　data　and　learn　preliminary　feature　representations.　Then,　we　introduce　a　prototype　alignment　module　(PAM)　that　computes　site-aware　prototypes　through　weighted　feature　clustering　to　guide　inter-site　feature　alignment,　and　achieve　inter-site　equilibrium　feature　representations.　Finally,　we　develop　an　adversarial　alignment　module　(AAM)　that　employs　site-discriminative　adversarial　training　based　on　a　gradient　reversal　layer　to　guide　intra-site　feature　alignment,　and　learn　site-invariant　features.　The　three　modules　above　are　optimized　collectively　in　an　end-to-end　manner,　ensuring　effective　learning　from　both　labeled　and　unlabeled　data　while　alleviating　the　distribution　differences　of　multisite　data.　Experiments　on　the　ABIDE　I,　ABIDE　II,　and　ADHD-200　datasets　demonstrate　that　the　CSBNC-PAL　outperforms　many　state-of-the-art　SSL　methods　in　FBN　classification.　©　2013　IEEE.