Accurate　prediction　of　building　load　is　essential　for　energy　saving　and　environmental　protection.　Exploring　the　impact　of　building　characteristics　on　heating　and　cooling　load　can　improve　energy　efficiency　from　the　design　stage　of　the　building.　In　this　paper,　a　prediction　model　of　building　heating　and　cooling　loads　is　proposed,　which　based　on　Improved　Particle　Swarm　Optimization　(IPSO)　algorithm　and　Convolution　Long　Short-Term　Memory　(CLSTM)　neural　network　model.　Firstly,　the　characteristic　variables　are　extracted　and　evaluated　by　Spearman’s　correlation　coefficient　method;　Then　the　prediction　model　based　on　the　CLSTM　neural　network　is　constructed　to　predict　building　heating　and　cooling　load.　The　IPSO　algorithm　is　adopted　to　solve　the　problem　that　manual　work　cannot　precisely　adjust　parameters.　In　this　method,　the　optimization　ability　of　the　PSO　algorithm　is　improved　by　changing　the　updating　rule　of　inertia　weight　and　learning　factors.　Finally,　the　parameters　of　the　neural　network　are　taken　as　IPSO　optimization　object　to　improve　the　prediction　accuracy.　In　the　experimental　stage　of　this　paper,　a　variety　of　algorithm　models　are　compared,　and　the　results　show　that　IPSO-CLSTM　can　get　the　best　results　in　the　prediction　of　heating　and　cooling　load.　©　2020　by　author(s)　and　Frontier　Scientific　Publishing.