<p>In group decision-making problems related to sudden events, the absence of evaluation attributes and the emergence of minority opinions are common problems. Due to the shortcomings of existing methods for such problems, this paper proposes a group decision-making model based on public big data and minority opinions for sudden events. Firstly, a linear algorithm for the comprehensive attribute weights is novelly constructed based on two aspects of the public level and the expert level, the attribute weights of the public level are derived from a normalization algorithm by combining the term frequency-inverse document frequency technology with the latent dirichlet allocation model, and the attribute weights of the expert level are determined by constructing a maximum nonlinear deviation model. Secondly, by incorporating both the support degree and trust degree in the adjustment coefficient, a new valuable minority opinion management scheme is proposed to make the decision-making more reasonable and objective. Thirdly, a consensus optimization model is established based on minimum adjustment to protect minority opinions and improve decision-making efficiency. Finally, an application is demonstrated to illustrate the efficiency and flexibility of the proposed model.</p>