This　research　presents　a　new　music　generation　model　and　a　novel　MIDI　data　format　for　MIDI　music　generation.　This　innovative　data　format　allows　us　to　process　MIDI　music　in　a　manner　analogous　to　video　analysis.　Initially,　the　model　employs　Convolutional　Neural　Networks　(CNN)　as　an　encoder　to　effectively　capture　local　and　global　features　within　the　musical　data.　Subsequently,　we　utilize　a　Transformer　as　a　decoder,　leveraging　its　self-attention　mechanism　to　handle　the　long-term　dependencies　present　in　music　data.　In　the　training　process,　an　interactive　chaotic　algorithm　is　introduced　to　update　the　model’s　weights,　assisting　the　model　in　avoiding　entrapment　in　local　optima.　This　enhances　the　learning　efficiency　of　the　model　and　improves　the　quality　of　the　generated　output,　enabling　the　model　to　generate　music,　including　accompaniment,　that　aligns　with　human　aesthetics　from　any　given　melody.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd　2024.