Collaborative　filtering　based　on　single　domains　has　become　widely　used　in　today＇s　recommendation　system.　Nevertheless,　it　has　two　problems　that　need　to　be　solved,　i.e.,　the　cold　start　problem　and　the　data　sparseness　problem.　As　the　result,　cross-domain　recommendation　technology　has　emerged,　which　aims　at　integrating　user　preference　characteristics　from　different　domains.　This　paper　proposes　a　collaborative　filtering　recommendation　method　based　on　multi-domain　semantic　fusion　(CF-MDS).　CF-MDS　achieves　cross-domain　item　similarity　calculation　through　semantic　analysis　and　ontology　and　integrates　data　from　different　domains　iteratively　based　on　domain　relevance　to　rate　users　on　target　domain　items　and　to　produce　a　cross-domain　user-item　rating　matrix.　Collaborative　filtering　technology　is　then　combined　with　multi-domain　fusion　recommendation　algorithm.　Experimental　results　show　that　the　proposed　method　can　deal　effectively　with　the　cold　start　problem　and　data　sparsity　problem　that　exist　in　traditional　recommendation　systems　as　well　as　can　improve　the　diversity　of　recommendation.　Compared　to　other　cross-domain　recommendation　methods,　the　proposed　method　can　better　meet　personal　needs　of　users　and　also　improve　the　accuracy　of　recommendation.