Analyzing　character　sentiment　information　through　audio　is　an　essential　and　challenging　task.　Using　convolutional　and　recurrent　neural　network　approaches　for　audio　sentiment　analysis　has　achieved　initial　results,　proving　that　deep　learning　methods　can　be　helpful　for　sentiment　analysis　of　audio.　With　the　rise　of　multimodal　research,　using　audio　and　text　feature　fusion　for　sentiment　analysis　has　better　results.　However,　this　approach　uses　the　pipeline　approach　for　analysis,　which　needs　to　use　an　additional　speech-to-text　model　to　get　text　information　first　so　that　the　error　of　text　information　conversion　will　affect　the　subsequent　judgment.　In　order　to　solve　this　problem,　we　propose　a　new　end-to-end　model,　which　adopts　the　model　architecture　of　seq2seq,　encodes　audio　information　by　the　encoder,　generates　text　information　of　audio　by　the　decoder　and　understands　the　textual　content,　and　finally　obtains　the　final　emotional　state　by　fusing　the　features　extracted　by　the　decoder　and　the　encoder.　Our　model　has　experimented　on　actual　psychological　assistance　hotline　data,　and　the　results　show　that　our　method　is　significantly　better　than　the　baseline　method,　which　is　a　meaningful　method.　©　2022　IEEE.