Research And Implementation Of Text Classification Subsystem Of Online Machine Learning Platform For Youth

In recent years,with the development of computer technology and people's urgent need for technology to change their lives,artificial intelligence has risen strongly.The focus of STEM education for teenagers has also shifted from basic programming education to a more contemporary AI education.How to let teenagers learn AI programming efficiently and easily is a problem that urgently needs to be solved in modern AI education.Mlfk,an existing machine learning education platform,enables children of any age to train their own machine models intuitively and allows them to use the models in their own scratch programs.However,the training process is too transparent for users to understand and learn it.In view of the above problems,this paper designs an online machine learning platform for youth,and studies the text classification subsystem of it.Firstly,the paper reviews the development of AI and its application on machine learning platform,and summarizes the development at home and abroad.Then,the algorithm interactive learning function and the automatic prediction function of optimal algorithm for Chinese text classification are designed for the text classification subsystem.Especially for the automatic prediction function,the paper proposes an algorithm adaptive model based on the text feature of data set.For this model,the paper designs the feature and a feature extraction method of Chinese text data set firstly,and use the KNN algorithm to train and get the adaptive model which can automatically predict the optimal algorithm.Then,the paper proposes an incremental training mode of KNN algorithm based on weighted cluster,and implements the incremental training of the adaptive model.In view of the problem that the users' data set may be insufficient,the paper proposes a data enhancement mode based on tag similarity to enhance the users' data set and further improve the effect of users' models.Based on the above design and achievements of theoretical research,the paper designs and implements each module,and tests the function and performance of text classification subsystem,finally,it was compared with the traditional algorithm.The experimental results show that the KNN algorithm based on weighted cluster and the data enhancement mode based on tag similarity effectively improve the efficiency and effect of users' models.This paper proposes some new ways in many aspects on the selection of the optimal algorithm,which improves the effect of models and solves the problem of low efficiency in selecting the optimal algorithm.