For　the　current　research　on　aspectual　emotion　analytic　problems　in　the　task　of　low　degree　of　text　utilisation,　insufficient　extraction　of　semantic　and　emotional　features　from　commented　text,　and　inefficient　recognition　of　polysemous　words,　We　propose　a　dual-channel　mixed　neurological　network　based　on　the　Bert＇s　sentimental　analysis　model　(BDCM)　for　this　paper.　the　BERT　model　performs　text　vectorisation;　the　dual-channel　hybrid　neural　network　model　is　constructed　by　BiLSTM　channel　and　BiGRU　channel　to　obtain　the　full　and　partial　traits　information　of　the　selected　texts.　Finally,　the　features　output　from　the　dual-channel　are　horizontally　fused　for　the　emotion　classification.　results　of　the　SemEval　2014　dataset　shows　that　BERT　is　more　capable　of　extracting　semantic　features　from　the　text.　Contrast　to　this　with　the　primitive　model,　fusion　of　various　attentional　mechanisms　in　dual-channel　hybrid　neural　network　model　is　more　capable　of　extracting　features　than　the　simple　one　and　the　performance　of　emotion　classification　significantly　improves,　which　can　lead　to　better　performance　in　the　domain　of　emotion　classification.　©　2024　IEEE.