The　top　oil　temperature　in　ultra-high　voltage　(UHV)　reactors　has　attracted　enormous　interest　due　to　its　wide　applications　in　fault　diagnosis　and　insulation　evaluation.　In　this　work,　the　precise　prediction　method　based　on　the　Seq2Seq　module　with　the　convolutional　block　attention　mechanism　is　proposed　for　the　UHV　reactor.　To　reduce　the　influence　of　vibratility　and　improve　computational　efficiency,　a　combination　of　the　encoding　layer　and　decoding　layer　named　Seq2Seq　is　performed　to　reconstruct　the　complex　raw　data.　The　convolutional　block　attention　mechanism　(CBAM),　composed　of　spatial　attention　and　channel　attention,　is　utilized　to　maximize　the　use　of　information　in　data.　The　Seq2Seq-CBAM　is　established　to　forecast　the　variation　tendency　of　the　oil　temperatures　in　the　UHV　reactor.　The　experimental　results　show　that　the　proposed　method　achieves　high　prediction　accuracy　for　the　top　oil　temperature　in　both　single-step　and　multi-step.