Design And Implementation Of Bad Text Informationshielding Technology For Mobile Phone Game

The mobile games have enrich people’s entertainment life, but there are also all kinds of bad information, affect the healthy growth of teenagers. How to control the spread of bad information, the content of the network information security has become one of the main content of the information filtering research, this thesis studies the bad text shielding and filtering.Based on the analysis of word segmentation, is commonly used in natural language processing (AC) algorithm based on pattern matching algorithm on the basis of the use of finite state machine, the principle of schema matching, converting the uncertain state machine to determine the state machine, so as to realize the mobile phone game bad text information filtering system, and finally the effectiveness of the proposed system is.verified by experiment. Specifically, the work of this thesis mainly includes the following contents:(1) Message filter screen in the system of mobile game structure is summarized. System abstracts of text according to the data acquisition module, data management module, detection module, statistics module four parts, and to implement.(2) Optimization and implement the masking algorithm based on finite state machine schema matching. This method is mainly based on multiple pattern matching algorithm (AC) algorithm as the foundation, analyzes the disadvantages of the algorithm, merged output function and failure function, on the basis of the analysis of the finite state machine schema matching algorithm.(3) Defines the client and server communication protocol, text information storage format, poor statistical method of text categorization. And the server data statistics module USES the non-relational database for storage and statistics, which makes the efficiency of the system resource utilization higher.Finally, based on finite state machine schema matching of mobile games bad text information filtering system experiment test, from two aspects of time complexity and space complexity to verify the effectiveness of the system, the results show that the algorithm is suitable for large-scale pattern matching in both English and Chinese. Since the system runs stable and reliable, has reached the expected goal. Found in the actual production of how to strengthen the semantic understanding and a more reasonable pattern matching, is the need to further solve the problem.