Research On Risk Of UGC App Garbage Projects Based On Optimized AHP-Fuzzy Algorithm

With the rapid development of the internet,people are increasingly using UGC(User-Generated Content)products,resulting in an increasing amount of spam information.Currently,the government is paying more attention to the issue of the online environment and has released corresponding policies.If a large amount of spam information proliferates in UGC products,the app is at risk of being removed.An app typically contains multiple business scenarios,such as nearby people,nearby updates,audio and video,each of which is a separate project.Currently,China still relies on traditional methods to identify spam information in different projects,but the diversity of black industry attack forms makes it difficult for us to effectively suppress the proliferation of spam information in various projects.Therefore,the purpose of this paper is to construct a risk assessment system to quantify black industry attack behavior in various projects,process different levels of risk for each project,and avoid the risk of pollution outbreaks.Most of the risk assessments for black industry spam are based on subjective experience and lack detailed quantitative analysis.The traditional AHP-Fuzzy algorithm is considered to have strong subjectivity and fuzziness,while random forest regression weighting can assign objective weights.Therefore,this paper introduces the random forest into the spam risk assessment system and combines it with the Analytic Hierarchy Process(AHP)to reduce the subjectivity of risk assessment.Using the spam content of "M Company," "Z Company," and "K Company" black industry attacks as the research object,a risk assessment system was constructed for the spam content appearing in each project of the app.Firstly,domestic and foreign anti-spam literature was studied.Secondly,the current status and existing problems of anti-spam in UGC apps were analyzed.Thirdly,the random forest was introduced to optimize the AHP and construct the risk assessment system.Fourthly,UGC app spam data was collected,and the Fuzzy evaluation method was used to evaluate the overall risk situation.Empirical analysis and comparative analysis were carried out on different models.From the empirical analysis,the random forest-optimized AHP-Fuzzy model improved the accuracy rate by 5.91% and the true negative rate by 9.96% compared to the entropy weighting method optimized AHP-Fuzzy;it improved the accuracy rate by13.47% and the true negative rate by 19.93% compared to the principal component analysis optimized AHP-Fuzzy;and it improved the accuracy rate by 23.68% and the true negative rate by 30.54% compared to the coefficient of variation optimized AHP-Fuzzy.It can be seen that the random forest and AHP model is more effective than other models,validating the effectiveness of the model,which is also practical for different projects of the app.Finally,based on this,the conclusion of this paper is summarized,and flexible control strategies are formulated for different risk levels,which can reduce the pollution rate by 51.56%to 94.29%,with significant effects.The research results have reference value for the accuracy of spam risk assessment,improving anti-spam audit and supervision management.