Mine water inrush has become one of the common water hazards in the operation of mining enterprises. The rapid and effective identification of the water inrush source is the basis for taking measures to control the water inrush when the mine water inrush accident occurs. Therefore, taking Jinggezhuang Mine in Kailuan Mining Area as the object of analysis, this paper collected and collated the water quality data obtained from many years' observation in the mining area. Mixing simulation of water sample data was carried out by PHREEQC, and its hydrochemical characteristics were analyzed. The establishment method of identification mixed inrush water source in mining area is studied. The following conclusions are drawn: (1) The hydrochemical characteristics of groundwater in Jinggezhuang Mine are generally characterized by high concentration of Ca2+, K++Na+, HCO-. The hydrochemical types of main aquifers in this mining area are mainly HCO3-Ca and HCO3-Na type. (2) The hydrochemical types of mixed water samples changed between HCO3-Ca and HCO3-Na, and the ion concentration also changed greatly. The main ions that changed were Ca2+, K++Na+, HCO-, which were anions and cations with higher ion concentration in water samples. (3) A TensorFlow-based artificial neural network mixed water source identification model is proposed. The data of water samples are trained and tested iteratively for several times to ensure that the identification accuracy of the neural network identification model for mixed water inrush sources reaches more than 80%.