Wireless　communication　network　attack　detection　is　important　in　the　field　of　wireless　communication　network　data　mining.　Transfer　learning　is　a　powerful　tool　to　solve　the　problem　of　link　prediction　in　unlabeled　wireless　networks.　In　the　feature　mapping　process　of　transfer　learning,　the　loss　of　source　domain　information　is　very　serious,　which　leads　to　the　loss　of　knowledge　in　inverse　mapping.　To　obtain　more　specific　and　complete　information　in　the　source　domain,　a　link　prediction　method　based　on　the　distribution　function　fitting　method　of　domain　orthogonal　term　family　is　proposed.　The　key　samples　in　the　source　domain　are　used　to　construct　a　family　of　orthogonal　polynomials,　and　the　least　square　approximation　of　the　distribution　function　is　obtained　by　using　the　family　of　orthogonal　polynomials.　The　feature　vector　set　of　the　key　samples　is　extracted　from　the　source　domain,　and　the　pseudo　label　is　assigned　to　the　target　training　set,　so　that　the　link　classifier　can　complete　the　supervised　link　prediction　task.　In　addition,　an　advanced　performance　of　TLAD　is　obtained　by　comparing　with　other　state-of-the-art　models　in　practice.