The　electricity　price　of　electricity　market　with　high　proportion　of　new　energy　has　great　volatility,　but　the　prediction　effect　of　traditional　deep　learning　model　is　not　good.　This　paper　proposes　a　price　forecasting　method　using　ATT　(attention　mechanism)　to　improve　LSTM　(long　short　term　memory)　and　DBO　(dung　bee　optimizer)　to　obtain　the　optimal　parameters.　Firstly,　the　attention　mechanism　is　used　to　improve　the　extraction　ability　of　the　input　features　that　play　a　key　role　in　the　electricity　price　forecasting.　Secondly,　the　DBO　algorithm　is　used　to　optimize　the　model　to　obtain　the　optimal　parameters.　Finally,　the　optimal　parameters　ATT-LSTM　model　is　used　to　obtain　the　optimal　forecasting　results.　This　paper　uses　the　real　data　of　the　electricity　market　with　a　high　proportion　of　new　energy　to　verify　the　proposed　method.　Compared　with　the　LSTM　algorithm,　the　prediction　accuracy　of　the　proposed　method　is　improved　by　65%　under　the　MAE　index,　and　compared　with　the　single　GRU　and　BP　algorithm,　the　prediction　accuracy　is　improved　by　74.9%　and　83.1%,　respectively,　which　has　high　prediction　performance.　©　2023　IEEE.