Recently, both commercial websites and search engines require people to enter query keywords to get useful information. It is becoming increasingly important to understand the user's intentions. Query classification task is quite helpful to commercial websites and search engines, and its goal is to classify the query to predefined categories to better understand the needs of the user. In this paper, we use the supervised learning method to learn query vector representation automatically based on semantics feature in query classification task. Our experiments use the three different neural networks, respectively convolutional neural network, Long Short-Term Memory (LSTM) and two layers LSTM to model the query. The experiments results show that our automatic learning query vector model outperforms other models. The two layers LSTM model perform better than the other two models, and the F-score improves 4% compared to Logistic Regression.