Abstract: In this paper, we propose a risk-aware method for industrial users to assess and prevent the risk situation when the load equipment within the industrial user is regulated, based on situational awareness technology. Firstly, the Bayesian LSTM model is trained to predict the corresponding values at the time of risk propagation based on the historical output data and fault rate of industrial consumers' load equipment. Secondly, a model of risk propagation that takes into account load uncertainty is proposed by analogy with the SI model. Thirdly, a system of indicators is proposed to present the risk situation of industrial users in terms of the risk of safety of the load equipment itself, the risk of the production process, and the risk of economic losses. Finally, based on the risk propagation model and the index system, the risk prevention and control plan is formulated by effectively selecting the target for regulation of load equipment within industrial users. A simulation analysis is conducted using industrial users of steel mills to verify the rationality and effectiveness of the proposed method.