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In recent years, convolutional neural networks(CNN) and recurrent neural networks(RNN) have been widely used in the field of text classification. In this paper, a model of CNN and long short term memory network(LSTM) feature fusion is proposed. Long-term dependence is obtained by replacing the LSTM as a pooling layer, so as to construct a joint CNN and RNN framework to overcome the single convolutional nerve. The network ignores the problem of semantic and grammatical information in the context of words. The proposed method plays an important role in reducing the number of parameters and taking into account the global characteristics of text sequences. The experimental results show that we can achieve the same level of classification performance through a smaller framework, and it can surpass several other methods of the same type in terms of accuracy.