Power generation from coal-fired boilers generates a large amount of polluting gases. This is based on tunable laser absorption spectroscopy combined with chromatographic imaging to reconstruct the temperature and concentration of premixed flames, and use these parameters to regulate the operating conditions of boilers as a means to reduce polluting gas emissions and improve energy efficiency. After the spectral analysis, two absorption spectral lines (7149.058cm-1 and 7150.4716cm-1) near 7148.8~7151cm-1 were selected as suitable for high temperature reconstruction of H2O, and different temperature and concentration fields were simulated and reconstructed by adopting adaptive iterative algorithm, BP-neural network algorithm and convolutional neural network algorithm. It was found that the convolutional neural network algorithm outperformed the other two algorithms in terms of reconstruction accuracy and stability. To investigate the effect of error on the reconstruction results, it was found that the error had less effect on the convolutional neural network algorithm by adding random errors, and the temperature and concentration reconstructions were highly accurate. In order to verify the feasibility of the convolutional neural network algorithm, different combustion conditions were selected for reconstruction comparison. The study shows that the reconstructed images of the convolutional neural network algorithm tend to be flatter and more consistent with the actual combustion conditions. The study also demonstrates the advantages and feasibility of the convolutional neural network algorithm in the reconstruction of combustion fields.