The　heating　industry　in　northern　regions　accounts　for　a　large　share　of　energy　consumption,　and　the　heating　system　mainly　relies　on　manual　experience　for　heating　load　regulation,　thus　resulting　in　energy　waste.　Load　prediction　is　a　key　part　of　industrial　IoT　technology,　and　heating　load　prediction　can　provide　accurate　predictions　for　a　heating　load　to　achieve　effective　energy　utilization.　Traditional　load　prediction　algorithms　have　low　accuracy　and　strict　requirements　for　data.　Therefore,　this　paper　proposes　a　convolutional　long-short　term　memory　neural　network　(CNN-LSTM)　model　integrating　residual　blocks　to　predict　heating　load.　The　model　uses　convolutional　neural　network　(CNN)　and　long　and　short-term　memory　neural　network　(LSTM)　to　extract　the　temporal　and　spatial　characteristics　of　heating　load.　Furthermore,　the　model　uses　residual　blocks　to　deepen　network　depth　and　improve　network　performance,　and　solves　the　problem　of　feature　and　information　loss　in　feature　extraction.　The　experimental　results　show　that　the　CNN-LSTM　model　with　fused　residual　convolution　blocks　has　the　lowest　prediction　error,　and　the　average　absolute　percentage　error　of　heating　load　prediction　results　of　a　heat　exchange　station　in　Lanzhou　City　reaches　0.57%.　©　2021　IEEE.