To optimize the detection performance of infrared imaging spectrometers, a priority fusion temperature control algorithm (PFA) with user-defined indicators and a temperature control accuracy of 1.0 mK is proposed. This algorithm combines basic proportional–integral–derivative (PID), fuzzy PID, and selfdisturbance rejection control algorithms with the BP neural network algorithm to achieve high-performance blackbody temperature control. Results of Simulink simulation experiments show that compared with traditional algorithms, the overshoot of the PFA algorithm decreases from 3.606% to 0.101%, the response time decreases from 64 min to 14.4 min, and the temperature control accuracy reaches 1.0 mK. Simultaneously, a blackbody radiation calibration platform is built, and the physical experimental results are consistent with the theoretical simulation results. This model lays the theoretical foundation for the practical application of the high-precision temperature controlled blackbody in the field of space remote sensing and has remarkable significance in the field of temperature control.