The　selection　of　process　parameters　is　a　difficult　problem　in　current　power　battery　welding　industry.　To　improve　the　efficiency　of　power　battery　welding　and　to　solve　the　difficulty　in　selecting　process　parameters　for　meeting　multiple　objectives,　a　solution　combining　kernel　ridge　regression　and　Multi-Objective　Particle　Swarm　Optimization　(MOPSO)　was　adopted.　In　this　scheme,　the　welding　lower　limit　corresponding　to　process　parameters　was　set,　and　the　kernel　ridge　regression　model　was　used　for　simulation　based　on　Gauss　kernel　function.　A　set　of　process　parameters　was　represented　as　a　particle　of　MOPSO,　and　by　means　of　a　regression　model,　an　optimal　solution　set　for　the　specified　welding　objective　was　effectively　obtained　through　three　operational　steps　consisting　of　the　evolution　and　variation　of　the　population,　the　selection　and　optimization　of　the　guide　and　the　maintenance　of　solution　set.　In　addition,　K-nearest　neighbor　algorithm　was　referenced　for　designing　an　evaluation　criterion　to　measure　the　reliability　of　each　solution　and　further　screen　better　solutions,　which　ensured　that　the　selected　process　parameters　own　higher　fault　tolerance.　The　proposed　method　had　solved　the　difficult　problem　in　selecting　process　parameters　faced　by　current　power　battery　welding　industry,　which　would　enhance　the　efficiency　and　effectiveness　of　power　battery　welding.　©　2021,　Editorial　Department　of　CIMS.　All　right　reserved.