Autism　spectrum　disorder　(ASD)　is　a　complex　neurodevelopmental　disorder　severely　impacting　social　abilities.　Previous　research　has　demonstrated　impairments　in　functional　brain　connections　among　individuals　with　ASD.　This　paper　proposed　a　novel　diagnostic　strategy　for　ASD　using　spatial-temporal　deep　neural　networks　(DNN)　integrating　the　self-attention　mechanism.　Specifically,　we　employed　sliding　window　method　in　the　temporal　dimension　to　construct　dynamic　functional　connectivity　(dFC)　data　and　applied　Kendall＇s　rank　correlation　coefficient　to　extract　features.　Subsequently,　deep　neural　networks　based　on　multi-head　self-attention　mechanism　were　designed　to　extract　more　discriminative　spatial　and　temporal　high-level　abstract　features.　The　learned　spatial-temporal　features　from　multiple　layers　of　multi-head　self-attention　were　concatenated　and　subsequently　incorporated　with　feed-forward　neural　networks.　We　designed　a　novel　disagreement　regularization　term　combined　with　cross-entropy　loss　for　classification.　We　conducted　systematic　experiments　on　the　large-scale　ASD　dataset.　Both　10-fold　and　inter-site　cross-validation　results　outperform　existing　classical　studies　in　ASD　classification,　suggesting　the　effectiveness　of　the　proposed　model.　Moreover,　we　identified　the　discriminative　functional　connections　associated　with　ASD　classification.　©　2023　IEEE.