Research And Implementation Of Text Classification Based On Depth Learning Theory And SVM Technology

With the rapid development of Internet technology,massive data information are generated.There are millions of Internet users to obtain valuable and meaningful information on their own through the Internet every day,how to let each person can quickly and accurately get the data from the massive own knowledge and skills,has become a hot topic of current research.In order to solve this problem,the researchers obtain data analysis,mining,classification,to help people improve the efficiency of information retrieval.The main work of this paper is: the use of deep learning and support vector machine combined with the method of massive data mining text classification and analysis,and finally get the essence of the text.1.This paper introduces the current research status and significance of the existing text classification technology at home and abroad,and expounds the importance of text classification.2.The traditional classification technology is studied,which includes three parts: text preprocessing,text feature extraction and text classification,and Bayes,KNN,SVM classification algorithm is expounded,and the scope of the three algorithms and the advantages and disadvantages are analyzed.3.This paper introduces the relevant theoretical knowledge of deep learning,proposes a method of using sparse automatic coding to map the original data in high dimensional space and using the output of a deep belief network for obtaining projection automatic sparse encoding text abstract features.This paper studies the process of text feature extraction based on sparse coding and deep belief network.4.In this paper,we combine the depth learning and the improved multi classification SVM method to design the classifier which is based on the combination of sparse automatic coding and deep belief network and SVM classifier.Finally,through the design of experiments,the proposed method is tested and compared with the traditional text classification methods.The accuracy of text classification is tested by parameter modification.