Research On Automatic Classification Of Internet News Based On SVM Model Optimization

With the rapid development of science and technology,the Internet has been widely used in various industries.The arrival of the era of big data makes people in the process of obtaining information resources,there will also be a lot of interference,harmful information,arbitrary dissemination of network information is easy to get information when low efficiency,information misdirection and other situations.How to classify the Internet news data accurately and improve the utilization rate of information has become the research goal of many researchers.With the development of artificial intelligence and intelligent platforms,SVM research has become hot again.SVM has made remarkable achievements in text and image classification.On the basis of analyzing and summarizing the word segmentation,representation,dimensionality reduction,classification and result determination in the process of automatic news classification,this paper focuses on the in-depth study of the classification of degraded and peacekeeping..The main research contents of this paper are as follows:(1)Aiming at the problems of large amount of Internet news text data and redundant data resources,which are not convenient for users to find effective information.The following improvements are made: In data preprocessing,Linear Discriminant Analysis(LDA)can make the mapped samples have the best classification performance.Before LDA carries out feature dimension reduction,One-Way ANOVA is used to analyze the correlation between each attribute and category,and remove the irrelevant or low-correlation features.Then LDA is used to map the original data to the low dimension that can better distinguish features from categories,so as to achieve data dimension reduction.(2)In order to improve the convergence speed and precision of whale optimization algorithm,a whale optimization algorithm based on nonlinear convergence factor and local disturbance is proposed.Firstly,nonlinear convergence factor are introduced to improve the diversity of whale populations and expand the range of whale searches for food.At the same time,in the whale surrounding predation stage,a local perturbation strategy is adopted to enhance the ability of the algorithm to jump out of the local extremum and improve the optimization precision of the algorithm.The experimental results show thatcompared with particle swarm optimization,bat algorithm and basic whale optimization algorithm,the improved algorithm is superior to other algorithms in terms of search speed,convergence accuracy and algorithm stability.(3)Through in-depth analysis of the thought,principles and processes of SVM,the following improvements are made for the shortcomings of the basic SVM model,such as low classification accuracy and time-consuming parameter optimization:.In the SVM parameter selection,the whale optimization algorithm is used to quickly find The global optimal solution improve the classification accuracy of the model,and the improved whale optimization algorithm has better effect in the optimization of model parameter.And by combining the automatic classification system of Internet news with the optimized SVM model,the Internet news information can be presented to news users more clearly and intuitively,which can not only improve the efficiency of users to obtaining effective information,but also improve their interest in using it,reduces the interference of redundant data on users and realize the use value of this paper.