This　study　proposed　an　approach　to　optimize　the　process　parameters　using　the　entropy　weight　method　combining　regression　analysis　in　the　resistance　spot　welding　process.　Based　on　the　central　composite　experimental　design,　tests　were　carried　out　with　three　levels　of　process　parameters　for　spot-welded　titanium　alloy　sheets.　Multiple　quality　characteristics,　namely　nugget　diameter,　maximum　displacement,　tensile　shear　load,　and　failure　energy,　were　converted　into　a　comprehensive　welding　quality　index.　The　weight　for　each　quality　index　to　obtain　the　comprehensive　welding　quality　index　was　determined　based　on　the　grey　entropy　method.　The　welding　heat　input　for　each　welding　joints　was　calculated　based　on　the　dynamic　power　signal　in　the　welding　process.　The　mathematical　model　correlating　process　parameters　and　the　comprehensive　welding　quality　index　was　established　on　the　basis　of　regression　analysis.　The　relationship　between　the　welding　process　parameters　and　welding　heat　was　also　quantified　using　a　regression　model.　The　effects　of　welding　process　parameters　on　welding　quality　and　welding　heat　were　also　discussed.　To　optimize　multi-performance　characteristics,　the　desirability　function　was　employed.　The　verification　test　results　proved　that　the　method　proposed　in　this　paper　effectively　optimized　the　welding　parameters　and　kept　the　welding　heat　input　as　low　as　possible　at　the　same　time.　Welding　current　is　the　most　significant　parameter　affecting　the　welding　quality　followed　by　welding　time.　This　can　be　owing　to　its　direct　influence　on　the　amount　of　heat　supplied　to　the　welding　zone　during　the　welding　process.　The　method　proposed　in　this　study　can　serve　as　a　guidance　and　recommendation　for　resistance　spot　welding　welders　to　guarantee　welding　quality　and　meet　the　needs　of　high　production　and　effective　energy　saving.