The short text classification is a key task in the field of Internet text data processing. The long short-term memory (LSTM) and convolutional neural network (CNN) are the two most important deep learning models for short text classification. The research on the deep learning in the field of computer vision and speech recognition shows that the deep level of neural network model has better ability to express data features. Inspired by this, a deep learning model named ResLCNN (residual-LSTM-CNN) is proposed based on the structure of three LSTM layers and one CNN layer for text deep learning classification problem. In this model, the LSTM layer is used to capture long distance dependency features of the sequence data and the CNN layer can extract local features of the sentence by convolution operators. The ResLCNN model combines the advantages of LSTM and CNN effectively. At the same time, based on the residual model theory, the ResLCNN model adds an identity mapping between the first LSTM layer and CNN layer to alleviate the problem of vanishing gradients. In order to explore the ability of ResLCNN model for deep short text classification, some experiments are made on several data sets to compare with LSTM, CNN and their combination models. The result shows that compared with the single LSTM and CNN combination model, the ResLCNN deep model improves the accuracy rate by 1.0%, 0.5% and 0.47% respectively on the data sets of MR, SST-2 and SST-5 and achieves better classification results.