Dialogue intent detection and semantic slot filling are two critical tasks in nature language understanding(NLU)for task-oriented dialog systems.In this paper,we present an attention-based encoder-decoder neural network model for joint intent detection and slot filling,which encodes sentence representation with a hybrid Convolutional Neural Networks and Bidirectional Long Short-Term Memory Networks(CNN-BLSTM),and decodes it with an attention-based re-current neural network with aligned inputs.In the encoding process,our model firstly extracts higher-level phrase representations and local features from each utterance using convolutional neural network,and then propagates historical con-textual semantic information with a bidirectional long short-term memory net-work layer architecture.Accordingly,we could obtain sentence representation by merging the two architectures mentioned above.In the decoding process,we in-troduce attention mechanism in long short-term memory networks that can pro-vide additional sematic information.We conduct experiment on dialogue intent detection and slot filling tasks with standard data set Airline Travel Information System(ATIS).Experimental results manifest that our proposed model can achieve better overall performance.