From the perspective of counterterrorism strategies, terrorist risk assessment has become an important approach for counterterrorism early warning research. Combining with the characteristics of known terrorists, a quantitative analysis method of active risk assessment method with terrorists as the research object is proposed. This assessment method introduces deep learning algorithms into social computing problems on the basis of information coding technology. The authors design a special "Top-k" algorithm to screen the terrorism related features and optimize the evaluation model through convolution neural network so as to determine the risk level of terrorist suspects. This study provides important research ideas for counterterrorism assessment and verifies the feasibility and accuracy of the proposed scheme through a number of experiments, which greatly improves the efficiency of counterterrorism early warning.
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