Abstract: To solve the problem of ANSYS-Fluent software being unable to provide both high-precision and high-efficiency radiation spectrum models, secondary development was carried out for the full-spectrum correlated-k distribution (FSCK) model based on look-up table method and machine learning method, and the model was embeded into Fluent and coupled with built-in radiative transfer equation (RTE) solvers for radiation heat transfer calculation for mintures of common combustion gases and soots. Radiation heat transfer results of one-dimensional slabs and two group of flames were calculated by the model, and using the line by line (LBL) model as a benchmark, which were compared with the results calculated by gray gas weighted sum (WSGG) model in Fluent. Results show that the FSCK model yields more accurate solutions than the built-in WSGG model in Fluent, regardless of the presence of soot.