This　paper　introduces　an　improved　slack-based　game　cross-efficiency　measurement　model　that　enhances　the　existing　cross-efficiency　framework　and　integrates　it　with　the　Data　Envelopment　Analysis　(DEA)　game　cross-efficiency.　The　model　ensures　the　fairness　of　its　results　through　the　implementation　of　a　more　stringent　selection　of　frontier　face　weights.　It　accounts　for　the　competitive　relationships　among　Decision　Making　Units　(DMUs),　achieving　a　Nash　equilibrium　solution　through　continuous　iterations.　Furthermore,　the　model　accounts　for　undesirable　outputs　and　various　strategic　orientations,　enhancing　its　applicability.　The　model＇s　effectiveness　is　validated　through　comparative　analyses　of　diverse　case　studies.　Additionally,　the　model＇s　practical　utility　is　demonstrated　through　the　analysis　of　industrial　data　from　various　Chinese　provinces　between　2010　and　2019.　Analysis　results　show　that　the　proposed　model　measures　production　efficiency　with　greater　precision　and　comparability　than　alternative　models.