Nowadays,　in　the　context　of　the　booming　digital　economy　and　video　becoming　the　main　carrier　of　data　explosion,　enhancing　the　precision　of　recommendation　algorithms　in　the　video　industry　has　emerged　as　a　prominent　area　of　investigation.　By　using　the　TransR　model　to　construct　the　movie　knowledge　graph　into　the　relationship　space　to　obtain　the　movie　entities　and　their　relationships,　the　multiple　relationships　between　movies　are　better　reflected,　so　as　to　calculate　the　semantic　similarity　between　movies,　and　then　the　collaborative　filtering　algorithm　based　on　Pearson　coefficient　calculates　the　similarity　of　user　behavior,　and　the　two　similarities　are　linearly　fused　to　finally　generate　the　final　recommendation　list　for　Top-N　recommendation.　Comparative　experimental　results　show　that　the　algorithm　has　improved　in　the　main　indexes,　such　as　recall,　accuracy,　and　mean　absolute　error　(MAE).　©　2023　ACM.