Abstract: Due to high-intensity mining and complex geological conditions, frequent occurrence of rock burst disasters in deep coal mines in China is posing serious threats to mine safety and efficiency. The insufficient locating accuracy of the seismic monitoring system in burst-prone coal mines is still presented, which leads to difficulties in accurately identifying and controlling coal burst hazards. To precisely characterize spatial evolution of seismic activities and reduce the impact of location error on the seismic pre-warning, this paper used the current seismic networks in burst-prone coal mines to conduct forward modeling experiments to explore the distribution characteristics of source location error vectors in the mining field. A new method for characterizing seismic fracture connectivity that considers location accuracy impacts was proposed and applied for coal burst pre-warning in a longwall face. The results show that significant vectorial differences of seismic source location accuracy at different area of the mining field is a key factor in false identifying coal burst risks. The proposed Fracture-connectivity-probability Index ( F_\mathrmsum ) characterizes the rupture scale by using the near-field radius that relates to the seismic energy level, which considers the locating error impacts on fracture connectivity between seismic sources at different distances. F_\mathrmsum can restore the distribution law of the fracture extension and connectivity probabilities of the coal-rock mass in the greatest extent, and it also corresponds well to the coal burst risks. F_\mathrmsum can balance forecast accuracy and recall rate and have better correlation with coal burst, which can be an ideal indicator for periodical coal burst risk assessment. The outcome of this research can provide references for evaluating the monitoring capability of seismic networks and improving the ability and efficiency of coal burst pre-warning and prevention.