Due　to　the　increasing　requirements　for　the　improvement　of　the　accuracy　of　large　coordinate-measuring　machines　(CMMs),　the　laser-tracing　multi-station　measurement　technology,　as　one　of　the　advanced　precision　measurement　technologies,　is　worth　studying　in　depth　in　terms　of　its　practical　application　for　the　compensation　of　errors　in　large　CMMs.　Since　it　is　difficult　to　maintain　a　constant　temperature　of　about　20　degrees　C　in　the　actual　workshop　under　the　influence　of　solar　radiation　and　convective　heat　transfer,　there　is　a　gradient　in　the　spatial　temperature　distribution,　and　the　overall　temperature　changes　with　the　influence　of　external　factors　with　synchronous　hysteresis,　it　is　difficult　for　the　actual　calibration　environment　to　meet　the　standard　environmental　requirements.　Therefore,　the　influence　of　temperature　and　other　environmental　factors　on　the　accuracy　of　laser　ranging　and　large-scale　CMM　calibration　should　not　be　ignored.　In　this　paper,　on　the　basis　of　analyzing　the　temperature　distribution　and　change　rule　of　large　CMM　measurement　space　under　different　working　conditions,　the　radial　basis　function　(RBF)　neural　network　algorithm　was　used　to　build　a　non-uniform-temperature　field　model,　and　based　on　this　model　and　the　measurement　principle　of　the　laser-tracking　instrument,　the　method　of　laser　tracking　and　interferometric　ranging　accuracy　enhancement　was　put　forward　under　a　non-uniform-temperature　field.　Finally,　based　on　the　multi-station　technique　of　laser　tracing,　an　accurate　solution　for　the　volumetric　error　of　large　CMMs　under　the　condition　of　non　-20　degrees　C　ambient　temperature　was　realized.　Simulation　results　proved　that　compared　with　the　traditional　temperature-compensation　method,　the　proposed　method　improved　the　measurement　accuracy　of　the　volumetric　error　of　a　large-scale　CMM　using　laser-tracing　multi-station　technology　in　a　non-uniform-temperature　field　by　33.5%.　This　study　provides　a　new　approach　for　improving　the　accuracy　of　laser-tracer　multi-station　measurement　systems.