Due　to　the　variability　of　the　network　environment,　video　playback　is　prone　to　lag　and　bit　rate　fluctuations,　which　seriously　affects　the　quality　of　end-user　experience.　In　order　to　optimize　network　resource　allocation　and　enhance　user　viewing　experience,　it　is　crucial　to　accurately　evaluate　video　quality.　Existing　video　quality　evaluation　methods　mainly　focus　on　the　visual　perception　characteristics　of　short　videos,　with　less　consideration　of　the　ability　of　human　memory　characteristics　to　store　and　express　visual　information,　and　the　interaction　between　visual　perception　and　memory　characteristics.　In　contrast,　when　users　watch　long　videos,　video　quality　evaluation　needs　dynamic　evaluation,　which　needs　to　consider　both　perceptual　and　memory　elements.　To　better　measure　the　quality　evaluation　of　long　videos,　we　introduce　a　deep　network　model　to　deeply　explore　the　impact　of　video　perception　and　memory　characteristics　on　users＇　viewing　experience,　and　proposes　a　dynamic　quality　evaluation　model　for　long　videos　based　on　these　two　characteristics.　Firstly,　we　design　subjective　experiments　to　investigate　the　influence　of　visual　perceptual　features　and　human　memory　features　on　user　experience　quality　under　different　video　playback　modes,　and　constructs　a　video　quality　database　with　perception　and　memory　(PAM-VQD)　based　on　user　perception　and　memory.　Secondly,　based　on　the　PAM-VQD　database,　a　deep　learning　methodology　is　utilized　to　extract　deep　perceptual　features　of　videos,　combined　with　visual　attention　mechanism,　in　order　to　accurately　evaluate　the　impact　of　perception　on　user　experience　quality.　Finally,　the　three　features　of　perceptual　quality　score,　playback　status　and　self-lag　interval　output　from　the　front-end　network　are　fed　into　the　long　short-term　memory　network　to　establish　the　temporal　dependency　between　visual　perception　and　memory　features.　The　experimental　results　show　that　the　proposed　quality　assessment　model　can　accurately　predict　the　user　experience　quality　under　different　video　playback　modes　with　good　generalization　performance.　©　2024　Chinese　Institute　of　Electronics.　All　rights　reserved.