Industrial　transfer　is　the　inevitable　trend　of　economic　development.　The　traditional　industrial　transfer　theory　tends　to　adopt　partial　data　and　methodologies　from　reductionism,　and　thus　can＇t　tackle　with　the　highly　non-linear　systematic　problems　like　the　mechanism　and　evolution　path　of　international,　regional,　and　domestic　industrial　transfer.　With　the　properties　of　structural　complexity,　dynamic　evolution　and　multiple　linkages,　complex　networks　can　better　reflect　the　interdependent　and　mutually　restricted　relation　between　different　levels　and　components　of　the　industrial　structure,　pinpoint　the　optimization　and　control　nodes.　Currently,　there　are　only　a　few　available　researches　on　such　weighted,　directed　and　dense　networks　reflecting　the　topological　complexity　of　global　value　chain,　with　the　results　being　unsystematic　and　impractical.　This　paper　utilizes　the　available　ICIO　data　to　build　the　Binary　GISRN　model　in　accordance　with　crucial　flows　of　materials,　energy,　and　information　among　industrial　sectors　all　over　the　world.　Also,　methods　of　defining　and　measuring　the　networks＇　redundancies　are　devised　to　figure　out　the　trigger　of　worldwide　industrial　transfer　pattern　according　to　the　link　prediction　method,　thus　blazing　a　new　trail　for　the　evolutionary　economics.