Objective:　This　study　introduces　a　novel　approach,　F-GAN-NTD,　which　integrates　Generative　Adversarial　Networks　(GANs)　with　Non-negative　Tensor　Decomposition　(NTD)　theory　to　enhance　the　analysis　of　functional　Magnetic　Resonance　Imaging　(fMRI)　data　related　to　depression.　Methods:　F-GAN-NTD　is　applied　to　extract　nonlinear　non-negative　factors　from　multidimensional　fMRI　tensor　data,　utilizing　Deep-NTD　technology　to　generate　factor　matrices　that　capture　latent　structures　and　dynamic　features.　A　multi-view　neural　network　architecture　processes　these　factor　matrices　from　all　modalities　simultaneously,　enabling　comprehensive　pattern　discrimination　between　depression　patients　and　healthy　controls.　The　method　is　tested　on　the　Closed　Eyes　Depression　fMRI　(CEDF)　and　Strategic　Research　Program　for　Brain　Sciences　(SRPBS)　datasets.　Results:　The　F-GAN-NTD　method　demonstrates　significant　improvements　in　fMRI　data　classification,　outperforming　traditional　approaches.　It　also　effectively　restores　incomplete　fMRI　tensor　data　and　reveals　abnormal　brain　network　connections,　offering　insights　into　the　pathophysiological　mechanisms　of　depression.　Conclusions:　F-GAN-NTD　enhances　the　extraction　of　meaningful　features　from　fMRI　data,　improving　classification　performance　and　providing　a　deeper　understanding　of　depression-related　brain　abnormalities.　The　integration　across　modalities　contributes　to　a　more　comprehensive　analysis　of　depression.