Depression　seriously　affects　People＇s　Daily　life　and　work,　and　may　even　lead　to　suicide.　The　method　based　on　deep　learn　ing　is　expected　to　assist　the　clinical　diagnosis　of　depression　more　effectively　and　objectively.　Usually,　2D　convolutional　neural　network　(CNN)　is　used　for　feature　extraction　for　depression　level　assessment,　but　this　method　can　only　extract　stat　ic　features　and　will　lose　dynamic　spatiotemporal　features.　In　contrast,　3D　CNN　model　can　directly　extract　temporal　and　s　patial　features,　which　can　improve　the　performance　of　depression　level　assessment.　In　this　paper,　in　order　to　compare　the　depression　level　assessment　performance　of　different　3D　convolutional　neural　networks,　we　conducted　tests　using　3D　V　GGNet,　3D　GooleNet,　3D　ResNet,　3D　SENet　and　3D　DenseNet　networks　based　on　AVEC2013　and　AVEC2014　datasets.　The　experimental　results　show　that　the　3D　ResNet18　network　obtains　the　best　evaluation　results,MAE=7.48,RMSE=9.7　0　on　the　AVEC2013　dataset,MAE=　7.03,RMSE=9.01　on　the　AVEC2014　dataset.　Compared　to　other　existing　methods,　3　D　ResNet18　shows　excellent　performance.　©　2024　SPIE.