In response to the current coal mine gas disaster warning mostly focuses on disaster classification warning, disaster-causing factors and related laws and regulations, preventive measures failed to be associated in time, the disaster warning information content is complicated, the information fragmentation is serious, the presentation form is single, and it can't form the disaster warning prevention and control system well. The author proposes a research on coal mine gas disaster prediction and early warning technology based on knowledge mapping, utilizing the Interpretive Structural Model (ISM) for the construction of coal mine gas disaster indicator system, and then applying the Bayesian Network Model (BN) for the early warning of coal mine gas disaster; constructing the coal mine gas disaster mapping through the extraction of the knowledge entities of the gas disaster indicator system, gas laws and regulations, rules and regulations, etc.; and combining the Bayesian Network Model and the gas disaster warning information with the gas disaster indicator system, and the knowledge entities of the coal mine disaster prediction and early warning information. The combination of Bayesian network model and gas disaster mapping realizes the mapping construction of coal mine gas warning and prevention system, which facilitates the timely sending of gas warning information and the rapid response of prevention and control measures.