Deep Network Learning based on TF-IDF Text Features for Electric Power Speech Text Pre-disposal Method

Aiming at the challenge of lack of effective application of massive power operation text data, this paper proposes a graph convolutional neural network processing method including power speech text data responsible for text analysis. After pre-processing the electric power speech text, the word frequency-inverse document frequency (TF-IDF) algorithm is further used to extract the electric power operation text feature items. The power operation information model based on text data feature recognition is comprehensively designed. The recognition and classification results of power speech text data are verified through experiments on power data text datasets. The experimental results show that the accuracy of text classification of the topic model based on TF graph convolutional neural network is 76.4%. The recall rate is 75.2% and the F1 value is 75.8%, which is 3% higher than the accuracy rate of graph convolutional neural network text classification method and 3. 4% higher than the recall rate, 3.2% higher than the F1 value, and 3.2% higher than the Labeled-LDA model text classification method. The feature extraction method improves the text classification accuracy by 3.5%, recall by 1% and F1 value by 2.3%.