In　order　to　achieve　high　precision　from　non-contact　temperature　measurement,　the　hardware　structure　of　a　broadband　correlative　microwave　radiometer,　calibration　algorithm,　and　temperature　inversion　algorithm　are　innovatively　designed　in　this　paper.　The　correlative　radiometer　is　much　more　sensitive　than　a　full　power　radiometer,　but　its　accuracy　is　challenging　to　improve　due　to　relatively　large　phase　error.　In　this　study,　an　error　correction　algorithm　is　designed,　which　reduces　the　phase　error　from　69.08　degrees　to　4.02　degrees.　Based　on　integral　calibration　on　the　microwave　temperature　measuring　system　with　a　known　radiation　source,　the　linear　relationship　between　the　output　voltage　and　the　brightness　temperature　of　the　object　is　obtained.　Since　the　metal　aluminum　plate,　antenna,　and　transmission　line　will　have　a　non-linear　influence　on　the　receiver　system,　their　temperature　characteristics　and　the　brightness　temperature　of　the　object　are　used　as　the　inputs　of　the　neural　network　to　obtain　a　higher　accuracy　of　inversion　temperature.　The　temperature　prediction　mean　square　error　of　a　back　propagation　(BP)　neural　network　is　0.629　degrees　C,　and　its　maximum　error　is　3.351　degrees　C.　This　paper　innovatively　proposed　the　high-precision　PSO-LM-BP　temperature　inversion　algorithm.　According　to　the　global　search　ability　of　the　particle　swarm　optimization　(PSO)　algorithm,　the　initial　weight　of　the　network　can　be　determined　effectively,　and　the　Levenberg-Marquardt　(LM)　algorithm　makes　use　of　the　second　derivative　information,　which　has　higher　convergence　accuracy　and　iteration　efficiency.　The　mean　square　error　of　the　PSO-LM-BP　temperature　inversion　algorithm　is　0.002　degrees　C,　and　its　maximum　error　is　0.209　degrees　C.