Aiming at the problem of low categorization accuracy and uneven distribution of the traditional textclassification algorithms，a text classification algorithm based on deep learning has been put forward. Deep beliefnetworks have very strong feature learning ability，which can be extracted from the high dimension of the originalfeature，so that the text classification can not only be considered，but also can be used to train classification model.The formula of TF-IDF is used to compute text eigenvalues，and the deep belief networks are used to construct theclassifier. The experimental results show that compared with the commonly used classification algorithms such assupport vector machine，neural network and extreme learning machine，the algorithm has higher accuracy andpracticability，and it has opened up new ideas for the research of text classification.