Raman imaging is a nondestructive spectral imaging technique that is widely used in the biomedical field. However, slow data acquisition has seriously hindered the expansion of its application for fast dynamic systems. In this study, we completed the reconstruction of Raman spectra and imaging based on multichannel Raman imaging. First, we optimized the training samples, and made a global assignment weighted to the optimized samples to establish the linear regression function. Second, normalization and polynomial regression were introduced to improve the accuracy of reconstructed spectra based on the multichannel imaging of each pixel. Third, Raman imaging with high signal-to-noise ratio was established by the area or intensity of characteristic peak. Benzonitrile was used in the experiment and the accuracy of the reconstructed Raman spectrum was evaluated in terms of relative root mean square error (RMSE). The results show the accuracy of the Raman spectrum reconstructed by this algorithm is 58% higher than that of the pseudo-inverse, and 44% higher than the Wiener estimation. Therefore, this algorithm provides theoretical support for the application of Raman imaging technology in fast dynamic systems. [ABSTRACT FROM AUTHOR]