Most　of　current　gait　recognition　models　do　not　consider　different　influence　of　the　existing　gait　profiles　on　recognition　results.　In　addition,　most　of　the　existing　gait　recognition　models　ignore　temporal　information　of　gait　profiles.　We　propose　a　temporal　feature　learning　based　on　attention　mechanism　for　gait　recognition.　Firstly,　key　feature　subnet　module　is　designed　to　extract　key　features　that　have　great　influence　on　recognition　results,　which　allows　the　model　to　extract　more　discriminative　information.　Then,　temporal　feature　extraction　module　is　given　to　focus　on　temporal　information,　and　extract　long　temporal　features,　short　temporal　features　and　frame　level　features.　After　that,　temporal　modeling　module　is　put　forward　to　model　the　relationship　between　temporal　features　based　on　attention　mechanism.　Finally,　the　effectiveness　of　the　proposed　method　is　verified　by　experiments.　©　2023　SPIE.