The rapid and accurate identification of coal mine aquifer water source is of great significance for coal mine water inrush warning and post-disaster rescue. It takes a long time for water source identification with the traditional method, and it is not suitable to construct an online early warning system. A method of using laser induced fluorescence technology to identify the type of coal mine water source is proposed. The laser is used to excite the water sample. Then the fluorescence spectrum is obtained, with pattern recognition the water source can be rapidly identified. Two kinds of water samples-goaf water and sandstone water of Xieqiao Coal Mine in Huainan Mining Area were collected, and five mixed water samples were prepared according to different mixing ratios. Firstly, according to the various noise and interference information that may exist in the obtained water source fluorescence spectrum, the spectral data were pretreated by SG, Normalize, Gapsegment derivation, Detrend and MSC. Secondly, PCA was used to reduce the dimension of fluorescence spectral data due to a large amount of data. As a comparison of the six pretreatment methods (including the original spectrum), the number of principal components was taken by 3, and the results showed that the cumulative contribution of SG pretreatment is the largest, which was 97.26%. The second was the original spectrum, which was 92.38%. The cumulative contribution of Normalize and Detrend were not much different, which were 88.04% and 87.59%, MSC was 66.41%, and Gapsegment was the worst with 22.65%. Finally, the linear model of LDA and nonlinear model of RBF-SVM were used to identified and compared with the data of reduced dimension by PCA. Using LDA for modeling, SG-PCA-LDA had the highest accuracy rate, which reached 98.86%. According to the LDA model established, the verification set data were identified, and the accuracy rate of SG-PCA-LDA was still the highest with 100%. Using RBF-SVM for modeling, Original-PCA-RBF-SVM, SG-PCA-RBF-SVM, and Normalize-PCA-RBF-SVM had the highest accuracy rate, both of which was 97.14%. Based on the RBF-SVM model established, verification set data were identified, and the accuracy rate of Original-PCA-RBF-SVM and SG-PCA-RBF-SVM was still the highest, which is 97.14%. Tt can be found that the accuracy rate of the LDA verification set was improved which compared with the modeling set, and the accuracy rate of the RBF-SVM verification set was slightly lower than the modeling set, which showed that LDA model had better generalization ability and higher accuracy rate for fluorescence spectral data of this coal mine water. The results showed that the SG-PCA-LDA model combined with laser induced fluorescence technology is a better method for local coal mine water source identification, and it verified the possibility of identification for goaf water, sandstone water and mixed water, which can be extended to identify other mixed water sources of coal mines.