BACKGROUND:　Many　studies　have　been　done　on　the　emotion　recognition　based　on　multi-channel　electroencephalogram　(EEG)　signals.　OBJECTIVE:　This　paper　explores　the　influence　of　the　emotion　recognition　accuracy　of　EEG　signals　in　different　frequency　bands　and　different　number　of　channels.　METHODS:　We　classified　the　emotional　states　in　the　valence　and　arousal　dimensions　using　different　combinations　of　EEG　channels.　Firstly,　DEAP　default　preprocessed　data　were　normalized.　Next,　EEG　signals　were　divided　into　four　frequency　bands　using　discrete　wavelet　transform,　and　entropy　and　energy　were　calculated　as　features　of　K-nearest　neighbor　Classifier.　RESULTS:　The　classification　accuracies　of　the　10,　14,　18　and　32　EEG　channels　based　on　the　Gamma　frequency　band　were　89.54%,　92.28%,　93.72%　and　95.70%　in　the　valence　dimension　and　89.81%,　92.24%,　93.69%　and　95.69%　in　the　arousal　dimension.　As　the　number　of　channels　increases,　the　classification　accuracy　of　emotional　states　also　increases,　the　classification　accuracy　of　the　gamma　frequency　band　is　greater　than　that　of　the　beta　frequency　band　followed　by　the　alpha　and　theta　frequency　bands.　CONCLUSIONS:　This　paper　provided　better　frequency　bands　and　channels　reference　for　emotion　recognition　based　on　EEG.