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Key population control is related to national security and social stability. Aiming at the difficulty of extracting key population information entities, this paper proposes a sequence labeling method based on dynamic word vectors. Replace the Word2vec pre-training model with the BERT model, which strengthens the feature extraction capabilities of the traditional entity extraction model, and more fully describes the multiple semantic and syntactic information of words. The improvement ideas for the BiLSTM-CRF model are as follows: Embed the BERT model upstream of the model, which is responsible for converting the original corpus into a dynamic vectorized representation, and the trained word vector is input into the BiLSTM layer for semantic encoding, and further mining the semantic related features of the entity context, and finally, the CRF layer outputs the sequence label with the maximum probability. After training with key population information as a data set, the F1 value of the model reached 0.90.

Aiming at the problem of event prediction in large-scale event network, a collapse subgraph convolution (CSGCN) algorithm is proposed, which uses event subgraph to predict the subsequent events of event group. CSGCN algorithm collapses the edge induced event subgraph in large-scale event network, removes the irrelevant event nodes from the subgraph, and forms a new event subgraph. GCN algorithm is used to learn the graph embedding representation of the event subgraph, and the subsequent events of the event group are predicted by comparing the similarity between the graph embedding representation of the event group and the subsequent events. Because only some related nodes are processed each time, the application of the model in large-scale data graph is feasible. Through experiments, we explore and verify the effectiveness of extracting features from subgraphs of large-scale graph by using graph convolution training to obtain graph embedding representation. We find that GCN has better event prediction effect than Euclidean distance and co rotation similarity, which further shows that graph convolution algorithm has good performance in the field of graph feature extraction.

In order to analyze the network structure of the “East Turkistan” organization and identify members with higher individual threats, so as to conduct a better attack on the “East Turkistan” organization. We use Gephi, a network analysis software, and collect open source information on three batches of 25 terrorists announced by the Ministry of Public Security and use networkX, a Python package to study the structure of complex networks, to conduct social network analysis(SNA) of the “East Turkistan” organization personnel. The “East Turkistan” organization presents a combination of scale-free network and hierarchical network in the topological structure. To manifest the analysis of social network methods' importance in combating terrorism, we simulate the effect of removing the core members of the “East Turkistan” organization. The construction of SNA requires a large deal of open source information. How to obtain intelligence is an important factor that restricts precision strikes. In addition, how to combine social network analysis with computer deep learning knowledge is also a new hot spot and direction in the field of anti-terrorism.

This paper uses the method of Agent-Based Modeling and Simulation to analyze the interactions among participants in Mass Violence Events. We study the affection degree of instigators, activists, cops and civilians to individual civilians to attend the events. The results show that the influencing factors which could affect civilians to attend the events are agitation power, number of activists, number of cops and risk-avoidance and so on. It shows that the law of development of Mass Violence Events. These results could provide public security organs with some certain theoretical support.

This paper introduces a new method for analyzing Mass Violence Events, and then we make use of Intelligent Artificial and Agent-Based System to deal with them. In the meanwhile, we describe and defy three kinds of agents (cops, activists and civilians), what comes next is that we apply these theories to model and simulate such events. The simulation results show that the behaviors of activists can easily affect whether civilians will join into Mass Violence Events, if they attend events, it will lead cops to face more difficulties when dealing with them. The new method can provide certain theoretical meanings for public security organs to address Mass Violence Events.

During any extreme event, especially the evacuation in crowded places. The keys to saving lives are orderly and efficient evacuations. We have improved the traditional particle swarm optimization. It can simulate non-adaptive crowd behavior in the crowd facilities during the emergency evacuation. We introduce the concept of the leader (trained staff) and the parameter of influence, and analyzed the influence of the leader on the evacuation process, especially the factors of the leader's distribution, number and influence. It will provide theoretical support for the evacuation of High-density facilities.

During and after any extreme event of natural or man-made origin, the primary objectives are getting people out of harms way and to an safety area. The keys to saving lives are orderly and efficient evacuations. We have proposed a new model based on the traditional lattice gas automate, in our paper, it can simulate the human crowd behavior in the crowd facilities, such as shopping malls, stadiums, school buildings etc, under the condition that many people rush to exits at the same time. In this model, people move by their instinct and the information what they obtained. In order to reappearance the scenes of the events and reach a better evacuation. We introduce psychological factors, velocity levels of pedestrians and exit levels in our model.