In order to address the problems that the features and information is limited in short text, the short text features are not fully expressed by traditional convolutional neural network (CNN) and recurrent neural network (RNN), a text classification model named convolutional gated recurrent neural network was proposed to represent sentence feature vector and classify short texts. The pooling operation was removed in convolution layerof the model to retain sequential structure and location information in text data. Series-parallel convolution structure was used to extract multi-feature combination of words and local context information as the input of RNN. Then, the gated recurrent unit (GRU) was used as the structure of RNN to represent the sentence features based on the sequential information of text. The features were input to the classifier with additive margin to guide network to learn distinguishing features and realize short text classification. The short text classification data set TREC, MR, and Subj were applied for testing. The influence of network hyper-parameters selection and convolution layer structures on classification accuracy were simulated and analyzed, and common text classification models were compared in experiments. Experimental results demonstrated that the classification accuracy of text data was improved by removing the pooling operation and using smaller convolution kernels for series-parallel convolution in the multi-feature representation. Compared with the GRU with the same number of parameters, the classification accuracy of the proposed model was increased by 2.00%, 1.23% and 1.08% in three datasets respectively. Compared with the CNN with the same number of parameters, the classification accuracy of the proposed model was increased by 1.60%, 1.57% and 0.80% in three datasets respectively. Compared with Text-CNN, G-Dropout, F-Dropout and other common models, the classification results also kept best. Therefore, experiments showed that the classification accuracy was effectively improved by the proposed model, which could be applied to short text classification scenarios.
