This　paper　presents　the　novel　spatial　and　temporal　fusion　model　(STFM),　an　effective　approach　for　Autism　Spectrum　Disorder　(ASD)　detection　and　classification　tasks　using　foundational　machine　learning　models.　Utilizing　ensemble　learning　principles,　STFM　improves　the　classification　performance　by　integrating　weak　classifiers.　The　process　begins　with　the　sliding　window　method　applied　to　fMRI　data,　constructing　brain　networks　through　Pearson　correlation　calculation　between　brain　regions.　This　infuses　the　network　with　both　temporal　and　spatial　patterns.　Then,　bidirectional　LSTM　(Bi-LSTM)　and　2DCNN　are　applied　for　temporal　and　spatial　feature　extraction　respectively.　The　model　further　ensures　smoother　data　variations　between　patterns　through　interpolation,　and　utilizes　a　basic　cross　attention　mechanism　for　fusion　of　patterns.　The　fused　patterns　are　then　classified　by　a　simple　SVM　classifier.　The　presented　STFM　model　demonstrates　a　remarkable　classification　accuracy　of　70.42%,　surpassing　most　fundamental　machine　learning　models　in　ASD　detection.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd　2024.