The　extrapolation　task　in　the　temporal　knowledge　graph　has　received　increasing　attention　from　scholars　due　to　its　wide　range　of　practical　application　scenarios.　At　present,　recurrent　neural　networks　are　currently　widely　used　in　temporal　knowledge　graph　completion　techniques.　These　networks　are　employed　to　depict　the　sequential　pattern　of　entities　and　relations.　However,　as　the　sequence　lengthens,　some　critical　early　information　may　become　diluted.　Prediction　errors　ensue　in　the　completion　task　as　a　result.　Furthermore,　it　is　observed　that　existing　temporal　knowledge　graph　completion　methods　fail　to　account　for　the　topological　structure　of　relations,　which　leads　to　relation　representations　with　essentially　little　distinction　across　different　timestamps.　In　order　to　tackle　the　previously　mentioned　concern,　our　research　introduces　a　Temporal　Knowledge　Graph　Completion　Method　utilizing　Sequence-Focus　Patterns　Representation　Learning　(SFP).　This　method　contains　two　patterns:　the　Focus　pattern　and　the　Sequential　pattern.　In　the　SFP　model,　we　developed　a　novel　graph　attention　network　called　ConvGAT.　This　network　efficiently　distinguishes　and　extracts　complex　relation　information,　thereby　enhancing　the　accuracy　of　entity　representations　that　are　aggregated　in　the　Focus　pattern　and　Sequential　pattern.　Furthermore　we　proposed　RelGAT,　a　graph　attention　network　that　simulates　the　topological　structure　of　relations.　This　enhances　the　precision　of　relation　representations　and　facilitates　the　differentiation　between　relation　embeddings　generated　at　various　timestamps　in　the　Focus　pattern.　Utilizing　a　time-aware　attention　mechanism,　the　Focus　pattern　extracts　vital　information　at　particular　timestamps　in　order　to　amplify　the　data　that　the　Sequential　pattern　dilutes.　On　five　distinct　benchmark　datasets,　SFP　significantly　outperforms　the　baseline,　according　to　a　comprehensive　series　of　experiments.