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This paper studied the data process methods of Laser-Induced Breakdown Spectroscopy (LIBS) and proposed a spectral intensity correction method utilizing laser energy monitoring and plasma morphology imaging, combined with neural network algorithm to improve the spectral stability. We set up a LIBS system, which had a laser beam sampling module to monitor laser output energy and a CMOS camera imaging module to capture plasma flame outlines. A back propagation neural network (BPNN) model was designed for standardizing the spectrum to a lower relative standard deviation (RSD) of emission line intensities, in which training spectrum, sample energy and image parameters were inputs, and the average of all the training spectrums were outputs. This method was applied in both aluminum and soil LIBS tests, and got effective results in reducing spectral intensity fluctuations. This data processing method not only provided a practical access to acquire stable spectrum information for both qualitative and quantitative LIBS analysis but also showed a bright future of combining LIBS data processing with machine learning methods.
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