Automatic　generation　of　code　comments　can　help　alleviate　software　project　development　and　maintenance　difficulties　caused　by　insufficient,　missing,　or　mismatched　code　comments.　Previous　works　have　demonstrated　that　identifying　the　intention　of　code　and　the　corresponding　comment　categories　can　enhance　the　accuracy　of　automatic　code　comment　generation.　However,　this　method＇s　effectiveness　relies　on　the　expertise　of　programmers　and　requires　a　significant　amount　of　human　effort.　We　propose　an　automatic　code　comment　generation　method　based　on　different　keyword　sequences　to　address　this　issue.　We　first　investigate　the　verbs　in　code　comments　of　mainstream　public　datasets　and　use　automated　methods　to　classify　the　comments　into　three　categories.　Then　we　extract　and　encode　the　keyword　sequences　of　the　code　according　to　the　comment　category　using　different　ways.　The　encoded　sequences　are　combined　with　three　classical　comment　generation　models　to　generate　comments　automatically.　Additionally,　an　algorithm　for　selecting　API　comments　is　proposed　to　address　the　issue　of　API　comment　quantity　causing　a　decrease　in　the　quality　of　generated　comments　during　the　keyword　selection　process.　Experimental　results　indicate　that　the　performance　of　the　baseline　model　can　be　improved　in　terms　of　BLEU,　ROUGE-L,　and　BLEU-DC　metrics.　In　addition,　compared　with　two　models　that　combine　other　information,　there　is　a　certain　degree　of　improvement　in　terms　of　three　metrics,　indicating　that　the　proposed　method　can　generate　code　comments　more　accurately.　　©　2023　IEEE.