To construct the public opinion risk assessment system for earthquake disaster network, the paper puts forward the risk assessment method of the public opinion of Accelerating Genetic Algorithm BP neural network (AGABP) for earthquake disaster after the public opinion monitoring index is built. According to the evolution theory of network public opinion, the paper focuses on the physical and social attributes of the earthquake disaster network public opinion, and puts forward earthquake disaster Internet public opinion risk monitoring indexes of 2 dimensions, 4 indexes of second-level and 10 indexes of third-level. According to the disadvantage of the conventional evaluation system for nonlinear, high dimension and non-normal evaluation problem, this paper makes use of the advantage of BP network, which can approximate any nonlinear continuous function with arbitrary precision. The paper uses BP network for evaluating the risk of earthquake disaster network public opinion, and also uses Accelerating Genetic Algorithm (AGA) improving the shortcoming of BP to solve the problem of slow training and premature convergence in conventional BP networks. The paper uses random sample data to carry out self-learning training for AGABP model and verifies it with actual sample data. The research results show that:AGABP model has obvious advantages in convergence speed and accuracy to compare with BP neural network and logistics curve, and can be applied to risk management practice of earthquake network public opinion.