The water inrush should been rapidly and accurately identified during preventing coalmine water inrush. The laser induced fluorescent (LIF) spectrum technology provides a new method to identify water inrush with the characteristics of high sensitivity, quick and accurate monitoring. In order to identify water inrush, this paper introduces the spectrum technology of LIF to obtain water inrush fluorescence spectra data. The spectral preprocessing methods of Savitzky-Golay(SG) and Multiplicative Scatter Correction (MSC) have been used to eliminate noise spectra in collecting process. Principal component analysis (PCA) extracts feature information, for SG reprocessing data, when the number of principal component is 3, the cumulative contribution rate can reach 99.76 percent. This method has largely retained the information of original data. This paper chooses the classification model with 3 layers BP neural network, constructing by different training and testing sets. The classification model with SG preprocessing has achieved accurate identification, however, appeared few false identification for MSC and original data. The result shows that SG preprocessing is better than MSC. Research results show that the classification model with PCA and BP neural network can effectively identify coalmine water inrush, and have the strong self-organizing, self-learning ability.