The　automatic　diagnosis　of　depression　using　deep　learning　has　recently　shown　significant　progress,　and　3DCNN　has　been　applied　to　assessing　depression　levels　from　videos.　There　are　two　problems　to　be　encountered　applying　3DCNN,　including　insufficient　data　to　train　the　model　and　an　insufficient　diversity　of　features　extracted　on　a　single　scale.　An　approach　is　presented　in　this　paper　to　address　these　issues.　Firstly,　we　augmented　the　raw　data　by　adding　noise　and　applying　rotations　to　increase　the　amount　of　the　training　set.　Secondly,　we　constructed　a　3DCNN　to　extract　more　diverse　features　from　multiscales.　We　evaluated　our　proposed　method　on　the　AVEC　2014.　Our　approach　achieved　MAE=7.13　and　RMSE=9.16,　demonstrating　its　effectiveness　in　improving　the　accuracy　of　depression　assessment　compared　to　existing　approaches.　©　2023　SPIE.