To　improve　the　diagnosis　accuracy　of　non-severe　depression　(NSD),　this　article　proposes　a　diagnosis　method　of　NSD　based　on　cognitive　behavior　of　emotional　conflict.　First,　the　original　classification　features　are　constructed　based　on　the　cognitive　behavior　of　emotional　conflict　and　statistical　distribution,　and　a　classification　normalization　method　is　proposed　to　preprocess　the　feature　data.　Then,　the　relief　algorithm　and　principal　component　analysis　(PCA)　are　recruited　for　feature　processing.　Finally,　four　classifiers　[k-nearest　neighbor　(KNN),　support　vector　machine　(SVM),　kernel　extreme　learning　machine　(KELM),　and　random　forest　(RF)]　are　used　to　classify　NSD　patients　and　normal　subjects.　The　test　results　show　that　among　all　the　classifiers,　RF　achieves　the　highest　classification　sensitivity　and　specificity　of　92%　and　88%,　respectively.　Compared　with　the　results　of　other　NSD　diagnosis　methods　in　recent　years,　it　has　a　better　performance.　The　diagnostic　method　for　NSD　proposed　in　this　article　has　obvious　performance　advantages　and　provides　technical　support　for　improving　the　accuracy　of　clinical　depression　diagnosis.　Furthermore,　it　also　provides　a　new　idea　and　method　for　the　diagnosis　and　screening　of　depression.