Abstract: When there is a large amount of text data, it is necessary to extract effective features from the text data to capture important information of the text to facilitate the storage and querying of the text. Therefore a machine learning based text classification and label prediction algorithm research is proposed. Conditional random field method is used to annotate and segment the part of speech of the processed text, and obtain the features of the texrt. Text features are inputted into a self attention mechanism recurrent convolutional neural network, and after model training, the classification results and label prediction results of the text outputted. After experimental verification, the proposed algorithm can effectively complete text classification and label prediction, with an average false rate of 95. 2% in text classification and an average loss of 0. 4% in text prediction ranking.

Key words: text classification, conditional random field, part-of-speech tagging, self attention mechanism cyclic convolutional neural network, machine learning