Abstract: In view of the problems existing in the current coal mine outburst prevention and early warning technology, such as unclear effectiveness of early warning model, large error of early warning critical value and poor adaptability of early warning system to coal mine, this paper analyzed the characteristics of coal mine outburst prevention information, designed the structure of outburst prevention early warning system driven by multi-source data, studied the adaptive technology of outburst prevention early warning index driven by multi-source data, realized intelligent optimization of quantitative evaluation of early warning model effectiveness and adaptive training of early warning critical value, and improved the adaptability of early warning system to coal mine. By establishing a quantitative evaluation method for the effectiveness of the early warning model, the early warning model is intelligently selected to avoid model selection errors caused by human subjective factors; By establishing the adaptive training method of early warning critical value, the early warning critical value can adapt to the actual situation of coal mine, and the accuracy of early warning critical value is gradually improved with the passage of time, thus reducing the workload of critical value investigation. In the application process of this technology system in a coal mine in Panzhou area, Guizhou Province, the early warning model a and early warning model s with better effects were quantitatively screened from 4 representative early warning models. On August 10th, under the condition that the gas concentration did not exceed the limit, early warning model a caught the abnormal situation of gas emission caused by the change of coal thickness in advance, and issued early warning information in time. After taking corresponding measures, the hidden dangers of accidents were eliminated. This technology can make coal mine outburst prevention early warning have higher adaptability, improve coal mine outburst prevention management level, and provide support for safety production decision.