In　target　speaker　extraction,　many　studies　rely　on　the　speaker　embedding　which　is　obtained　from　an　enrollment　of　the　target　speaker　and　employed　as　the　guidance.　However,　solely　using　speaker　embedding　may　not　fully　utilize　the　contextual　information　contained　in　the　enrollment.　In　this　paper,　we　directly　exploit　this　contextual　information　in　the　time-frequency　(T-F)　domain.　Specifically,　the　T-F　representations　of　the　enrollment　and　the　mixed　signal　are　interacted　to　compute　the　weighting　matrices　through　an　attention　mechanism.　These　weighting　matrices　reflect　the　similarity　among　different　frames　of　the　T-F　representations　and　are　further　employed　to　obtain　the　consistent　T-F　representations　of　the　enrollment.　These　consistent　representations　are　served　as　the　guidance,　allowing　for　better　exploitation　of　the　contextual　information.　Furthermore,　the　proposed　method　achieves　the　state-of-the-art　performance　on　the　benchmark　dataset　and　shows　its　effectiveness　in　the　complex　scenarios.