Abstract: In the process of mass information classification,the information text model and similarity are often used to classify,which can not fully represent the information attribute,leading to conflicts when classifying information.The intelligent classification method of massive information based on conflict game theory was proposed to extract information features.On this basis,according to the orthogonal property of mass information,the massive information classification strategy was determined.The Nash equilibrium strategy and Pareto optimal strategy were introduced to seek out the optimal solution to the problem of massive information classification and improve the classification strategy.The conflict information detection method was used to determine whether there is a conflict in the conflict information detection classification.If there is a conflict,it is transformed into a constraint satisfaction problem.Through the analysis of constraint variables of the classification problem,the contents of operational conflict in the classification is determined,and the expression of conflict discrimination in the mass information classification is established to realize the research of massive information intelligen classification.The experimental results show that using the proposed method for intelligent classification of massive information can get better classification results,it’s process is relatively simple,and this method has little effect on the computer network operation,providing reference experience for the practical application of the conflict game algorithm in the distribution of massive information.

Key words: Conflict game, Discrimination, Intelligent classification, Massive information, Nash equilibrium strategy, Restriction