To　enhance　the　excavator　performance　considering　the　digging　force　and　boom　lift　force　under　typical　working　conditions,　this　paper　aims　to　solve　the　complex　multiobjective　optimization　of　the　excavator　by　proposing　a　new　knowledge-based　method.　The　digging　force　at　multiple　key　points　is　utilized　to　characterize　the　excavator＇s　performance　during　the　working　process.　Then,　a　new　optimization　model　is　developed　to　address　the　imbalanced　optimization　quality　among　subobjectives　obtained　from　the　ordinary　linear　weighted　model.　The　new　model　incorporates　the　loss　degree　relative　to　the　optimal　solution　of　each　subobjective,　aiming　to　achieve　a　more　balanced　optimization.　Knowledge　engineering　is　integrated　into　the　optimization　process　to　improve　the　optimization　quality,　utilizing　a　knowledge　base　incorporating　seven　different　types　of　knowledge　to　store　and　reuse　the　information　related　to　optimization.　Furthermore,　a　knowledge-based　multiobjective　algorithm　is　proposed　to　perform　the　knowledge-guided　optimization.　Experimental　results　demonstrate　that　the　proposed　knowledge-based　method　outperforms　existing　methods,　resulting　in　an　average　increase　of　15.1%　in　subobjective　values.