Current research has ignored the hiddenness and the stochasticity of the evolution of public crisis management research, making the knowledge trajectories still unclear. This paper introduces a combined approach, LDA-HMM, to mine the hidden topics, present the evolutionary trajectories of the topics, and predict the future trends in the coming years to fill the research gaps. We reviewed 8543 articles in WOS from 1997 to 2021, extracted 39 hidden topics from the text using the LDA; 33 remained by manual labeling. The development of the topics over the years verifies that the topics are co-evolving with the public crisis events. The confusion and transition features indicate that most topics are confused or transferred to the others. The transition network and the direction of the topics show that six main transfer paths exist, and in the evolution process, the topics have become more focused. By training the HMM, we predict the trends in the next five years; the results show that the heat of the topic that focuses on traditional crisis issues will decrease while the focus on non-traditional issues will increase. We take the average error to test this model’s prediction effect by comparing it with the other approaches, concluding that it is better than the others. This study has practical implications for preventing crisis events, optimizing related policies, and grasping key research areas in the future.

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China has witnessed dramatic advances in emergency management in the past two decades, while the knowledge trajectories and future trends of related research are still unclear. This study takes the published articles in China National Knowledge Infrastructure as a data sample and introduces text mining and machine learning methods, namely Latent Dirichlet Allocation combined with the Hidden Markov Model, to detect and predict the knowledge trajectories of Chinese modern emergency management research. We analyzed 5180 articles, equivalent to approximately 1,110,000 Chinese characters, from 2003 to 2021, and mined 35 latent research topics. By labeling the topics manually and analyzing the evolutionary hotspots, confusion and transition features, and transition direction and network of the topics, we explored the knowledge trajectories of emergency management research in China. By training the HMM model, we predicted the research trends in the next five years. The main conclusions are: a mapping relationship exists between the hotspots of the published articles and the main events of emergency management in China; most emergency management research topics could confuse and transfer with others in the evolution process, and seven significant paths exist in the transition network. The research topics in the following years will be more detailed and concerned with the intellectual needs of modernization.

The complexity and uncertainty of compound disasters highlight the significance of local emergency resilience. This paper puts forward a framework, including the Projection Pursuit Model based on Real-coded Accelerating Genetic Algorithm and the Moran’s Index (Moran’s I), to measure the local emergency resilience and analyze its spatial distribution. An empirical test is conducted with the case of Hubei Province, China. The results show that: (1) the measurement indices related to infrastructure, material reserves, and resource allocation have a larger weight, while those related to personnel and their practice have a smaller weight. (2) The measurement value of local emergency resilience of sub-provincial regions in Hubei Province is vital in the eastern and weak in the western, and there are apparent east-west segmentation and north-south aggregation characteristics. (3) Although the sub-provincial regions do not show significant spatial correlation, the eastern regions centered on Wuhan are negatively correlated, and the western regions are positively correlated. Furthermore, this study provides theories and methods for local emergency resilience evaluation and spatial correlation exploration, and it has specific guidance recommendations for optimizing local emergency management resource allocation and improving local emergency resilience.