The　environmental　temperature　is　an　important　physical　quantity　to　measure　the　thermal　comfort　of　the　indoor　environment,　and　accurate　prediction　of　the　environmental　temperature　change　is　essential　to　control　the　environmental　thermal　comfort　and　improve　user　comfort.　This　paper　proposes　a　temperature　prediction　method　with　multi-dimensional　environmental　characteristic　based　on　convolutional　neural　network　(CNN)　and　long　short-term　memory　(LSTM)　network　with　an　attention　mechanism,　wherein　the　time　correlation　between　the　environmental　temperature　data　and　other　multi-dimensional　environmental　characteristic　is　considered.　First,　the　CNN　model　is　used　to　capture　the　data　characteristic　of　environmental　temperature　data　and　other　environmental　parameters.　Then　the　LSTM　model　is　used　to　extract　the　multi-dimensional　environmental　time　series　data.　To　improve　the　prediction　accuracy　of　the　proposed　method,　the　weight　of　the　attention　mechanism　to　the　output　of　the　LSTM　model　is　added　to　adjust　the　prediction　results.　Experimental　results　show　that　the　proposed　method　has　lower　complexity,　higher　training　efficiency　and　prediction　accuracy　in　predicting　the　temperature　changes　in　the　next　hour　and　adjacent　moments,　and　prove　that　the　proposed　method　is　applicable.　©　2022　IEEE.