With the development of human-machine dialogue, it is of great significance for the computer to accurately understand the user's query intention in human-machine dialogue systems. Intention classification aims at judging the user's intention in human machine dialogue and improves the accuracy and naturalness of the human machine dialogue system. This study first analyzes the advantages and disadvantages of multiple classification models in the intention classification task. On this basis, this study proposes a hybrid neural network model to comprehensively utilize the diversity outputs of multiple deep network models. To further improve the perfoance, the language model embedding is used in the input feature preprocessing and the semantic mining ability possessed for the hybrid network which can effectively improve the expression ability of the model. The proposed model achieves 2.95% and 3.85% performance improvement on the two data sets respectively compared to the best benchmark model. The proposed model also achieves the top performance in a shared task.