The　great　progress　in　recommendation　system　help　users　discover　more　interesting　items　that　satisfy　their　appetites.　Considering　the　video　recommendation　is　an　increasing　popular　sub-field　of　recommendation,　but　the　traditional　recommendation　techniques　such　as　Collaborative　Filtering　and　Content-based　model　simply　exploit　one　information　source　that　limits　its　performance.　In　this　paper,　we　proposed　a　Multi-info　fusion　based　recommendation　system　which　integrates　several　different　information　sources　to　comprehensively　model　the　similarity　between　videos.　The　information　sources　including　the　common　user-item　rating　data　and　video＇s　textual　content　that　consists　of　video＇s　genres　and　textual　description.　Experimental　results　on　a　public　dataset　show　that　the　proposed　system　is　of　high　quality　and　achieves　significant　improvements　over　the　traditional　Collaborative　Filtering　techniques.