Efficient　flotation　beneficiation　heavily　relies　on　accurate　flotation　condition　recognition　based　on　monitored　froth　video.　However,　the　recognition　accuracy　is　hindered　by　limitations　of　extracting　temporal　features　from　froth　videos　and　establishing　correlations　between　complex　multi-modal　high-order　data.　To　address　the　difficulties　of　inadequate　temporal　feature　extraction,　inaccurate　online　condition　detection,　and　inefficient　flotation　process　operation,　this　paper　proposes　a　novel　flotation　condition　recognition　method　named　the　multi-modal　temporal　hypergraph　neural　network　(MTHGNN)　to　extract　and　fuse　multi-modal　temporal　features.　To　extract　abundant　dynamic　texture　features　from　froth　images,　the　MTHGNN　employs　an　enhanced　version　of　the　local　binary　pattern　algorithm　from　three　orthogonal　planes　(LBP-TOP)　and　incorporates　additional　features　from　the　three-dimensional　space　as　supplements.　Furthermore,　a　novel　multi-view　temporal　feature　aggregation　network　(MVResNet)　is　introduced　to　extract　temporal　aggregation　features　from　the　froth　image　sequence.　By　constructing　a　temporal　multi-modal　hypergraph　neural　network,　we　encode　complex　high-order　temporal　features,　establish　robust　associations　between　data　structures,　and　flexibly　model　the　features　of　froth　image　sequence,　thus　enabling　accurate　flotation　condition　identification　through　the　fusion　of　multi-modal　temporal　features.　The　experimental　results　validate　the　effectiveness　of　the　proposed　method　for　flotation　condition　recognition,　providing　a　foundation　for　optimizing　flotation　operations.