Temporal-frequency　characteristics　in　fMRI　data　are　key　to　distinguishing　Autism　Spectrum　Disorder　(ASD)　from　neurotypical　individuals.　However,　the　non-linearity　and　multidimensionality　of　fMRI　data　pose　significant　challenges.　To　address　these,　we　introduce　a　Deep　Non-linear　Factorization　method　with　a　Wavelet　Temporal-Frequency　Attention　module　(Deep　WTFAF)　tailored　for　multidimensional　fMRI　analysis.　By　leveraging　the　wavelet　domain,　our　approach　applies　temporal-frequency　attention　to　assign　weights　to　significant　features,　enhancing　critical　data　while　reconstructing　incomplete　fMRI　data.　This　method　enables　deep　non-linear　factorization　and　effective　feature　representation　for　subsequent　classification　tasks.　Validated　on　ASD-related　fMRI　datasets,　Deep　WTFAF　outperforms　traditional　methods,　maintaining　essential　information　and　ensuring　robustness　against　high-dimensional　and　incomplete　data.　Stability　theory　proof　further　confirms　the　model＇s　reliability,　crucial　for　clinical　applications　like　neurological　disorder　classification.　©　2025　Elsevier　Ltd