Research on Automatic Text Classification Based on TI-LSTM

• 1、School of Mathematics and Statistics ，Changchun University of Science and Technology ，Changchun 130022

Abstract：In order to solve the problem of Chinese text classification and improve the accuracy, a text automatic classification algorithm based on TF-IDF and neural network is proposed named by TI-LSTM algorithm in this paper. Firstly, the corresponding features are extracted and quantified in the algorithm according to the semantic situation. Then the quantified features are trained and weighted with the long-short term neural network (LSTM). Finally, Chinese text information is evaluated based on feature weight. This method has been successfully applied to the classification of poverty-stricken students in our school. Compared with traditional KNN, logistic regression, naive Bayes and LSTM classification methods, the accuracy of training and testing has been greatly improved. The automatic text classification algorithm in TI-LSTM algorithm can extract features accurately with the original text semantic , and the average accuracy rate is over 86%.

Keywords： Computational Mathematics Neural network Text classification Feature extraction Text quantification Poverty-stricken students