Disclosed of the present application is relation to deep learning based text classification. The training corpus is screened by key clauses according to the weights of clauses in the training corpus, so as to keep the complete sentence and the original word order as much as possible according to the language habits. Thus, the deep learning model can learn normal semantic features. In addition, the subsample sets corresponding to different preset word length intervals is obtained from the training sample set, and each subsample set is putted into the deep learning model for training, so that several text classification models corresponding to different preset word length intervals can be obtained for text classification. Therefore, the deep learning models can be self-adaptively selected to classify texts based on the above mentioned multiple word length intervals and multi-model training method, to improve text classification accuracy.