Real-time spectral imaging of dynamic scenes is critical for industrial safety monitoring ， atmospheric remote sensing ， and aerial target recognition. Traditional spectral imaging methods are constrained by sequential sampling and low optical throughput ， limiting simultaneous achievement of high temporal resolution and high detection sensitivity. Computational spectral imaging offers a promising solution ， yet LWIR implementations remain challenging due to difficulties in coding element fabrication ， response matrix calibration ， and training data scarcity. A LWIR computational video spectral imaging method based on a broadband random coding array was developed ， addressing coding element fabrication ， system calibration ， and dataset construction. Using 9 broadband randomly coded channels ， 32 band spectral data cubes were reconstructed at 200 nm spectral resolution and 30 Hz frame rate ， with detection sensitivity 5-10 times higher than equivalent narrowband filter schemes. In a gas leakage scenario ， 16 gas species were identified with 98.97% average accuracy using a characteristic absorption spectral library. These results confirm the potential of this approach for gas spectral measurement and recognition in real dynamic scenes.