Establishing category keywords is the key problem in text classification

which should be solved first. On the basis of the classification of text by using the category keywords and TF-IDF algorithm

an improved TF-IDF algorithm has been proposed to overcome the shortcomings of the vector space model

which cannot well adjust the weights. Firstly

category keyword library should be established

and the expansion and duplication be carried out. The weight of keywords in the document is modified by the addition of the length of the document

and the shortage of the original features of the entry class distinction ability is solved effectively. By using Bayesian classification method

combined with the experiments

the effectiveness of the algorithm is verified

and the accuracy of text classification improved.