Research And Implementation Of News Classification System Based On Machine Learning

With the development of Internet technology and the high popularity of smart devices,all kinds of text information are explosively increasing.The phenomenon of information explosion is becoming more and more serious.News text is an important form of data bearing,it has an important position.How to obtain valuable information accurately and quickly from huge information sources has become an urgent need for people,which is also a hot issue in the industrial and academic fields.The research and implementation of news text categorization system can not only be applied in the fields of advertisement and information recommendation,but also provide pre-support for other projects.It is an indispensable link in the field of text processing.After categorizing the news texts,different advertisements can be displayed to the user based on the news category.In the recommendation system,the accuracy of the system can be improved by classifying news articles.And in the information retrieval,different search strategies can be formulated according to the classification results.In order to obtain valuable information accurately and quickly from a huge amount of information sources,it is necessary to design a classification system to classify various news texts,which is also the value of the news classification system.After analyzing the various steps of the text classification technology and the commonly used classification algorithms,this paper selects TextCNN and Lightgbm as the basic algorithms to build the final classification model.Based on the comprehensive consideration of computational efficiency,platform lightweight,and classification performance,the main technical route of Python as the development language,Flask lightweight framework as the system framework,and the SQLite as the back-end database was determined.Then the paper uses Python and Tensorflow to build the classification model,and builds a news classification system based on this model.Finally,the paper tests the system in detail,and the test results show that the performance of the classification model in this paper is in line with the expected results.