Multi-objective　evolutionary　algorithms　suffer　from　performance　degradation　when　solving　dynamic　multi-　objective　optimization　problems　(DMOPs)　with　a　new　conditional　configuration　from　scratch,　which　motivates　the　research　on　knowledge　extraction.　However,　most　knowledge　extraction　strategies　only　focus　on　obtaining　effective　information　from　a　single　knowledge　source,　while　ignoring　the　useful　information　from　other　knowledge　sources　with　similar　properties.　Motivated　by　this,　a　weighted　multi-source　knowledge　extraction　strategy-based　dynamic　multiobjective　evolutionary　algorithm　is　proposed.　First,　a　similarity　criterion　based　on　angle　information　is　constructed　to　quantify　similarity　between　different　source　domains　and　the　target　domain.　Second,　a　knowledge　extraction　technique　is　developed　to　select　a　specific　number　of　individuals　from　each　source　domain　using　a　distance　metric.　Third,　a　generation　strategy　based　on　dynamic　weighting　mechanism　is　proposed,　which　generates　a　certain　number　of　individuals　and　merges　these　individuals　into　the　initial　population　within　the　new　environment.　Finally,　the　comprehensive　experiments　are　conducted　on　public　DMOP　benchmarks　and　demonstrate　the　devised　method　significantly　outperforms　the　state-of-the-art　competing　algorithms.