The　community　detection　algorithms　based　on　label　propagation　(LPA)　receive　broad　attention　for　the　advantages　of　near-linear　complexity　and　no　prerequisite　for　any　object　function　or　cluster　number.　However,　the　propagation　of　labels　contains　uncertainty　and　randomness,　which　affects　the　accuracy　and　stability　of　the　LPA　algorithm.　In　this　study,　we　propose　an　efficient　detection　method　based　on　COPRA　with　Time-sequence　(COPRA-TS).　Firstly,　the　labels　are　sorted　according　to　a　new　label　importance　measure.　Then,　the　label　of　each　vertex　is　updated　according　to　time-sequence　topology　measure.　The　experiments　on　both　the　artificial　datasets　and　the　real-world　datasets　demonstrate　that　the　quality　of　communities　discovered　by　COPRA-TS　algorithm　is　improved　with　a　better　stability.　At　last　some　future　research　topics　are　given.