Recently,　efforts　have　been　made　to　explore　introducing　music　content　into　deep　learning-based　music　recommendation　systems.　In　previous　research,　with　reference　to　tasks　such　as　speech　recognition,　music　content　is　often　fed　into　recommendation　models　as　low-level　audio　features,　such　as　the　Mel-frequency　cepstral　coefficients.　However,　unlike　tasks　such　as　speech　recognition,　the　audio　of　music　often　contains　multiple　sound　sources.　Hence,　low-level　time-domain-based　or　frequency-domain-based　audio　features　may　not　represent　the　music　content　properly,　limiting　the　recommendation　algorithm＇s　performance.　To　address　this　problem,　we　propose　a　music　recommendation　model　based　on　chord　progressions　and　attention　mechanisms.　In　this　model,　music　content　is　represented　as　chord　progressions　rather　than　low-level　audio　features.　The　model　integrates　user　song　interactions　and　chord　sequences　of　music　and　uses　an　attention　mechanism　to　differentiate　the　importance　of　different　parts　of　the　song.　In　this　model,　to　make　better　use　of　the　historical　behavioral　information　of　users,　we　refer　to　the　design　of　the　neural　collaborative　filtering　algorithm　to　obtain　embedding　of　users　and　songs.　Under　this　basis,　we　designed　a　chord　attention　layer　to　mine　users＇　fine-grained　preferences　for　different　parts　of　the　music　content.　We　conducted　experiments　with　a　subset　of　the　last.fm-1b　dataset.　The　experimental　results　demonstrate　the　effectiveness　of　the　method　proposed　in　this　paper.　©　2022　Knowledge　Systems　Institute　Graduate　School.　All　rights　reserved.