id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_u66sz3ktqbbzvan2wds5ow7shq	Yu Jun	A Study of Intelligent Reading Model	2019	8	.pdf	application/pdf	4926	566	67	texts, this paper constructs an intelligent reading model. algorithm, are filtered to build an intelligent reading model. Keywords-Natural language processing(NLP); Decision Tree; if a data is trained by three classifiers, the output result of Data acquisition Data processing Feature extraction Training classifier Building Model word segmentation, it is necessary to use the algorithm based new words into the custom dictionary, followed that process 3) Feature extraction：For the processed data in step (2), the TF-IDF algorithm is used to extract the feature value word-text matrix generated by step (3).so that three value, training classifier, establishing model and carrying out whose value is the number of words occurring in the training By using the TF-IDF algorithm, the text information is Decision tree algorithm mainly includes feature selection means that, if a data is trained by three model, the output decision tree Bagging Gauss Bayesian Combination algorithm reading model based on the combination of decision tree, 	./cache/work_u66sz3ktqbbzvan2wds5ow7shq.pdf	./txt/work_u66sz3ktqbbzvan2wds5ow7shq.txt