The invention provides a coal component analysis method based on coal spectroscopic data. The method comprises the following steps: collecting coal spectroscopic data; performing coal component prediction by using a coal component analysis model, and ensuring that the input of the model adopts the collected spectroscopic data and the output is a coal component. The method establishes the coal component analysis model through the spectroscopic data and coal industrial analytic determination results, obtains spectroscopic characteristic data through extraction of a convolutional neural network,outputs coal components corresponding to the coal spectroscopic data determined by coal industrial analysis through a spectroscopic characteristic data extreme learning machine, and adopts a weight and deviation value of an artificial bees colony optimized extreme learning machine during the predication process, so as to obtain an optimized coal component analysis model. The coal component analysis model is integrated with a spectrum technology and is applied to the coal industrial analysis field, and a novel, rapid and accurate coal component analysis method is provided.