To improve the accuracy of text classification and expand different classification tasks, a text classification method combining one-dimensional convolutional neural network (1D-CNN) and bi-directional long short-term memory (Bi-LSTM) network is proposed. Firstly, in order to solve the difficulty of representing synonyms and polysemy, GloVe model is used to represent word features, making full use of the advantages of global information and co-occurrence window. Then, 1D-CNN is used for feature extraction to reduce the input feature dimension of classifier or prediction model. Finally, the classification module Bi-LSTM is optimized, which hidden layer is composed of two residual blocks, and the attention mechanism is introduced to further improve the accuracy of prediction. Binary classification and multiple topic classification experiments are carried out in multiple public data sets. The experimental results show that compared with other excellent methods, the proposed method has better performance in accuracy, recall and F1 score, with the highest accuracy of 92.5% and the highest F1 score of 91.3%.