<p>With the depletion of shallow coal resources, mining is gradually extending to deeper levels, with increasing gas content and gas pressure. Aabnormal gas outbursts phenomena occurs from time to time, posing a serious threat to coal mine safety production. Therefore, the risk identification of abnormal gas outbursts is of urgent practical significance. However, the process of identifying abnormal gas outbursts risk imposes higher requirements on the ability of adaptability, self-learning, and self-organization. The artificial immune technology based on the biological immune mechanism provides a powerful information processing and problem-solving paradigm, showing certain advantages in dynamic risk identification, which can meet the needs of dynamic risk identification of abnormal gas outbursts. In order to achieve dynamic risk identification of abnormal gas outbursts under complex underground conditions, provide decision-making basis for rapid warning and early prevention of abnormal gas outbursts, the principles of immune recognition of biological immune systems were adopted. Research was conducted on the identification of spatial risk zones for abnormal gas outbursts, the adaptive recognition algorithm based on T-B cell principles, and the type recognition of abnormal gas outbursts based on the Dynamic Time Warping algorithm. A risk identification model for abnormal gas outbursts based on the biological immune mechanism was constructed and tested in the 9111 mining face of a mine in Huaibei. The results show: (1) The adaptive recognition algorithm based on T-B cell principles can adaptively recognize the characteristic vectors of abnormal gas outbursts by adaptive adjustment of detectors and cloning and mutation of learning vectors, achieving the recognition and memorization of known or unknown feature vectors under dynamically changing environmental conditions. (2) The adaptive recognition algorithm for abnormal gas outbursts based on T-B cell principles and the type recognition algorithm for abnormal gas outbursts based on the Dynamic Time Warping algorithm, combined with the characteristics of biological immune systems, construct a risk identification model for abnormal gas outbursts based on the biological immune mechanism, which has the characteristics of adaptability, learning, and memorization. (3) Taking a abnormal gas outburst event in a certain 9111 working face of a mine in Huaibei as an example, the model was verified by inputting the characteristic vectors of abnormal gas outbursts and the output of the risk identification of abnormal gas outbursts based on the biological immune mechanism. The research results show that the dynamic risk identification model for abnormal gas outbursts based on the biological immune mechanism can meet the requirements of problem-solving in constantly changing complex environments, achieve dynamic risk identification of abnormal gas outbursts, and provide a basis for risk warning and intelligent decision-making for abnormal gas outbursts in mines.</p>