Under the background of mining intelligentization, the seismic monitoring technique has been widely used as the basic support for constructing the intelligent early warning systems for dynamic disasters such as rock bursts. The key issue for future research on seismic monitoring is to achieve accurate and efficient seismic signal capturing and events locating. To solve the inaccurate automatic picking up of low SNR seismic waves and avoid man-made interference during the events locating process, the authors proposed a transfer learning-based real-time automatic seismic locating method in mines using big data and deep learning. First, based on about one million seismic wave data of earthquakes, an initial model was designed to automatically pick up the arrival time of seismic waves in mines. Then, by using a set of more than 10,000 arrival times of seismic waves detected during mining, the initial model was further updated to fit the seismic location and information interpretation in mines, which achieves an automatic and accurate P wave pick-up for mining seismic waves. Finally, an automatic optimization method of locating sensors combination and a fine-tuning strategy for locating wave velocity was proposed, which can locate the seismic events automatically and accurately in mines. The proposed method was tested by using the blasting data from a longwall panel in an Inner Mongolia underground coal mine. The results show that the average planar and volumetric locating errors are 27.88 m and 28.40 m, respectively, which meets the accuracy requirements of seismic monitoring in mines. The method significantly reduces the impact of insufficient wave velocity accuracy of sensors on the locating events, and the results present strong robustness. Also, the seismic locating calculation time is shortened from minutes by manual work to within 200 milliseconds. The outcome can provide a reliable technical basis for real-time accurate seismic locating and intelligent hazard early warning in mines.