Emotion　is　an　essential　aspect　of　human　life,　and　effectively　identifying　corresponding　emotions　from　different　scenarios　will　help　promote　the　development　of　human-computer　interaction　systems.Therefore,　emotion　classification　has　gradually　become　a　challenging　and　popular　research　field.　Compared　with　text　emotion　analysis,　emotion　analysis　of　audio　data　is　still　relatively　immature.Traditional　audio　sentiment　analysis　research　is　based　on　feature　information　such　as　MFCC,　MFSC,　etc.　while　using　time-memory　models　such　as　LSTM　and　RNN　for　emotion　analysis.Due　to　the　rapid　development　of　transformers　and　attention　mechanisms,　many　scholars　have　shifted　their　research　from　the　RNN　family　to　the　transformer　family　or　deep　learning　models　with　attention　mechanisms.Therefore,　this　paper　proposes　a　method　to　convert　audio　data　into　a　spectrogram　and　use　a　vision　transformer　model　based　on　transfer　learning　for　emotion　classification.This　paper　conducts　experiments　on　the　IEMOCAP　dataset　and　the　MELD　dataset.　The　experimental　results　show　that　the　emotion　classification　accuracy　of　the　Vision　transformer　in　the　IEMOCAP　and　the　MELD　datasets　reach　56.18%　and　37.1%.　©　2023　SPIE.