In　recent　years,　image　aesthetic　quality　assessment　has　become　increasingly　popular.　In　addition　to　numerical　assessment,　aesthetic　captioning　has　been　proposed　to　capture　the　overall　aesthetic　impression　of　an　image.　To　further　advance　this　field,　we　address　a　task　of　aesthetic　attribute　assessment,　which　is　the　aesthetic　multi-attributes　captioning.　Labeling　the　comments　of　aesthetic　attributes　is　a　non-trivial　task,　which　limits　the　size　of　available　datasets.　We　construct　a　novel　DPChallenge　Multi-Attributes　Captions　Dataset　(DPC-MACD)　dataset　by　a　semi-automatic　way.　We　propose　two　novel　aesthetic　multi-attributes　captioning　networks,　which　are　the　Bottom-Up　and　Top-Down　Attention　Network　(BUTDAN)　and　Object-Semantics　Aligned　Pretrained　Network　(OSAPN).　The　experimental　results　show　that　our　method　can　predict　the　comments,　which　are　more　closely　aligned　to　aesthetic　topics　than　those　produced　by　the　previous　models.　Through　the　evaluation　criteria　of　image　captioning,　the　specially　designed　model　outperforms　other　methods.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd　2024.