Rapid identification of mine water inrush has great significance for mine safety production. The identification method of laser induced fluorescence(LIF) in mine water inrush requires to pretreat and characterizing the spectral curve is complicated. Therefore, a method to quickly identify the type of mine water inrush by using the convolutional neural network(CNN) was proposed. According to the coal mine water distribution characteristics and the most common type of water inrush, we selected three kinds of raw water samples and two kinds of mixed water mixed by the original water as experimental material, in the experiment, we used LIF technology to quickly obtain 200 sets of fluorescence spectrum curves of 5 kinds of water samples. After gray degree transformation, the fluorescence spectrum curves inputed into CNN algorithm,150 groups of spectrum as the training set while the rest 50 groups of spectrum as the test set. In the model test, CNN’s recognition rate was 100%. The experimental results showed that the CNN algorithm can not only save the data processing and feature extraction in the image of recognition of mine water inrush, but also quickly and effectively identify the type of mine water inrush.