To address the issue of low accuracy in quantitative analysis of coal quality testing using laser-induced breakdown spectroscopy (LIBS) due to matrix effects or environmental factors, this article proposes a method for fast quantitative analysis of organic elements in coal based on LIBS technology combined with the ASG-LWNet algorithm. Firstly, the LIBS spectra of 34 coal samples are collected using a laser-induced breakdown spectroscopy instrument. Then, an adaptive SG filtering algorithm is used to denoise the collected spectra, continuously updating the filter parameters to adapt to different signal characteristics and achieve better filtering effects. Finally, the corresponding characteristic spectral lines of the elements C, H, and S are selected as inputs to the LWNet model for quantitative analysis. Experimental results show that the correlation coefficients of C, H, and S elements based on the ASG-LWNet model on the test set are 0. 998 4, 0. 973 2, and 0. 995 4, respectively. And the root mean square errors are 0. 379 4, 0. 217 9, and 0. 611, respectively. Compared with before denoising, the prediction accuracy is significantly improved. The results indicate that, in the case of complex spectral noise, this method can reduce the impact of matrix effects and improve the accuracy of quantitative analysis.