In　the　existing　tensor　factorization　techniques　used　in　knowledge　graph　learning　and　reasoning,　only　direct　links　between　entities　are　taken　into　account.　However,　the　graph　structure　of　knowledge　graph　is　ignored.　In　this　paper,　knowledge　graph　reasoning　based　on　paths　of　tensor　factorization　is　proposed.　The　path　ranking　algorithm(PRA)　is　employed　to　find　all　paths　connecting　the　source　and　target　nodes　in　a　relation　instances.　Then,　those　paths　are　decomposed　by　tensor　factorization.　And　the　entities　and　relations　are　optimized　by　the　alternating　least　squares　method.　Experimental　results　on　two　large-scale　knowledge　graphs　show　the　algorithm　achieves　significant　and　consistent　improvement　on　tasks　of　entities　linking　prediction　and　paths　question　answering　and　its　prediction　accuracy　outperforms　that　of　other　related　models.　©　2017,　Science　Press.　All　right　reserved.