Research And Design Of Web Text Information Filtering System Based On Chaos Particle Swarm Optimization Algorithm

With the rapid development of the information age,more and more multimedia information appears in people’s lives,and the rich information resources bring great convenience to people’s work and life,but at the same time,it also takes a lot of time and energy to screen the resources needed in the numerous information.Therefore,how to filter the text information of illegal web pages on the internet,so that users can quickly and accurately locate the information of interest has become an important research field.To solve the above problems,this paper proposes a text information filtering technology。This paper mainly discusses the key technologies related to content-based web page text information filtering,and integrates the improved chaotic particle swarm optimization algorithm in the construction of the filter template.Finally,a web text information filtering system based on chaos particle swarm optimization algorithm is designed and implemented.The main work includes the following four aspects:(1)Research the key technology of the current mainstream text information filtering technology,and build the basic model of text information filtering.Nowadays,most forms of information on the Internet are text information,so this article mainly conducts an in-depth study on the filtering of text information.It mainly involves the following three main techniques,which are text representation(including the extraction of effective features of web pages),the establishment of classification model and the feedback of text filtering effect.(2)Aiming at the problems of poor local search ability of standard particle swarm optimization(SPSO)algorithm,which is easy to fall into the optimal value and lose search diversity,a comprehensive improved chaotic particle swarm optimization algorithm based on dynamic change of inertia weight is proposedThis method makes full use of the advantages of randomness,ergodic and regularity of chaotic motion,and selects a cubic map with good uniformity to optimize the optimal example of the current population.By speeding up the evolution speed of the particle population,the ability of the standard particle swarm optimization algorithm to overcome local extreme points is improved.In addition,the exponential transformation of the maximum fitness value and minimum fitness value of the population particles in the iterative process is used to dynamically adjust the value of inertia weight,and the random factor h is introduced to increase the randomness of the value,so as to improve the convergence speed of the algorithm.The experimental results show that the improved method has obvious optimization effect,improves the convergence speed of the algorithm,greatly improves the accuracy of the problem solved by the algorithm,and can improve the global search performance.(3)Improved Chaotic Particle Swarm Optimization Algorithm Combined with Filter Template ConstructionThis method uses a comprehensive improved chaotic particle swarm optimization algorithm based on the dynamic change of inertia weight to optimize the feature subset,and uses a parallel acceleration mechanism to solve the problem that the time complexity of the algorithm increases with each generation of particles.A fitness evaluation system is proposed,which focuses on the similarity of feature subsets,the number of features and the classification accuracy of feature subsets on the classifier.The experimental results show that this method can extract the optimal feature subset quickly and effectively when applied to feature selection.(4)Design and implement the network text information filtering system based on the above improved strategiesOn the basis of the comprehensive improved chaotic particle swarm optimization algorithm based on the dynamic change of inertia weight,it is further applied to the template construction of information filtering,and the overall framework and main modules of the system are designed to realize the text information filtering system based on the improved chaotic particle swarm optimization algorithm.