Text classification is an important task in the field of natural language processing. It has a wide range of applications, such as knowledge question and answer, text topic classification, text emotion analysis, and so on. There are many methods to solve the task of text classification, such as Support Vector Machines (SVM) model and Naïve Bayes model. Typical neural network models widely used now are the Recurrent Neural Network (RNN) and the Text Conventional Neural Network (TextCNN). In this study, the sequence model and convolution model in the field of text classification are analyzed, and a hybrid model of combining sequence model and convolution model is proposed. By comparing the performance of the different models on the open dataset, it is proved that the performance of the combined model is better than that of the single model.