In the era of big data, the social security data becomes more diverse and its amount increases rapidly, which challenges the analysis and decision of social security events significantly. How to accurately categorize the attack behavior in a short time and support the analysis and decision making of social security events becomes an urgent problem needed to be solved in the field of national and cyberspace security. Aiming at the behavior of aggression in social security events, a new Distributed Neural Network Classification (DNNC) algorithm was proposed based on the Spark platform. The DNNC algorithm was used to analyze the related features of the attack behavior categories, and the features were used as the input of the neural network. Then the function relationship between the individual features and attack categories were established, and a neural network classification model was generated to classify the attack categories of social security events. Experimental results on the data provided by the global terrorism database show that the proposed algorithm can improve the average accuracy by 15.90 percentage points compared with the decision tree classification, and by 8.60 percentage points compared with the ensemble decision tree classification, only decreases the accuracy on part attack type.

Some Chinese time expressions consisting of "cardinal+time unit word" may be time point expressions or time slot expressions in different context. An approach of classification of Chinese time expressions based on dependency parsing was proposed, for the purpose of automatic classification of Chinese time expressions. First some syntactic constraints of Chinese time expressions in sentences were found with the help of dependency parsing. Then some computable dependency rules were extracted from those syntactic constraints. Finally the classification of Chinese time expressions was executed using dependency rules. The experimental results show that in this approach the precision, recall, F-Measure of the confirmation are 82.3%, 88.1%, 85.1%; and the precision, recall, F-Measure of the classification are 77.1%, 82.5%, 79.7%.