Emotional　decoding　and　automatic　identification　of　major　depressive　disorder　(MDD)　are　helpful　for　the　timely　diagnosis　of　the　disease.　Electroencephalography　(EEG)　is　sensitive　to　changes　in　the　functional　state　of　the　human　brain,　showing　its　potential　to　help　doctors　diagnose　MDD.　In　this　paper,　an　approach　for　identifying　MDD　by　fusing　interhemispheric　asymmetry　and　cross-correlation　with　EEG　signals　is　proposed　and　tested　on　32　subjects　[16　patients　with　MDD　and　16　healthy　controls　(HCs)].　First,　the　structural　features　and　connectivity　features　of　the　theta-,　alpha-,　and　beta-frequency　bands　are　extracted　on　the　preprocessed　and　segmented　EEG　signals.　Second,　the　structural　feature　matrix　of　the　theta-,　alpha-,　and　beta-frequency　bands　are　added　to　and　subtracted　from　the　connectivity　feature　matrix　to　obtain　mixed　features.　Finally,　the　structural　features,　connectivity　features,　and　the　mixed　features　are　fed　to　three　classifiers　to　select　suitable　features　for　the　classification,　and　it　is　found　that　our　mode　achieves　the　best　classification　results　using　the　mixed　features.　The　results　are　also　compared　with　those　from　some　state-of-the-art　methods,　and　we　achieved　an　accuracy　of　94.13%,　a　sensitivity　of　95.74%,　a　specificity　of　93.52%,　and　an　F1-score　(f1)　of　95.62%　on　the　data　from　Beijing　Anding　Hospital,　Capital　Medical　University.　The　study　could　be　generalized　to　develop　a　system　that　may　be　helpful　in　clinical　purposes.