Depression　has　become　the　prevailing　global　mental　health　concern.　The　accuracy　of　traditional　depression　diagnosis　methods　faces　challenges　due　to　diverse　factors,　making　primary　identification　a　complex　task.　Thus,　the　imperative　lies　in　developing　a　method　that　fulfills　objectivity　and　effectiveness　criteria　for　depression　identification.　Current　research　underscores　notable　disparities　in　brain　activity　between　individuals　with　depression　and　those　without.　The　Electroencephalogram　(EEG),　as　a　biologically　reflective　and　easily　accessible　signal,　is　widely　used　to　diagnose　depression.　This　article　introduces　an　innovative　depression　prediction　strategy　that　merges　time-frequency　complexity　and　electrode　spatial　topology　to　aid　in　depression　diagnosis.　Initially,　time-frequency　complexity　and　temporal　features　of　the　EEG　signal　are　extracted　to　generate　node　features　for　a　graph　convolutional　network.　Subsequently,　leveraging　channel　correlation,　the　brain　network　adjacency　matrix　is　employed　and　calculated.　The　final　depression　classification　is　achieved　by　training　and　validating　a　graph　convolutional　network　with　graph　node　features　and　a　brain　network　adjacency　matrix　based　on　channel　correlation.　The　proposed　strategy　has　been　validated　using　two　publicly　available　EEG　datasets,　MODMA　and　PRED+CT,　achieving　notable　accuracy　rates　of　98.30　and　96.51%,　respectively.　These　outcomes　affirm　the　reliability　and　utility　of　our　proposed　strategy　in　predicting　depression　using　EEG　signals.　Additionally,　the　findings　substantiate　the　effectiveness　of　EEG　time-frequency　complexity　characteristics　as　valuable　biomarkers　for　depression　prediction.