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Intent detection is a key sub-task of spoken language understanding in human-machine dialogue system. Considering the problem of user's multi-intent expressed, a multi-intent classifier based on single-intent marker is constructed by using capsule network to identify user's multiple intents expressed. In order to ensure the feature quality of the intent text, the deep semantic information of intent text is extracted by adding convolution capsule layer in capsule network, at the same time feature capsules are dynamically allocated to intent capsule category by using dynamic routing in capsule network. The probability of multiple intents is determined by setting threshold value, thus completing the task of multi-intent detection. Experimental results show that the capsule network is better than the convolutional neural network in the multi-intent detection task, the capsule network with convolution capsule layer can improve the performance of multi-intent detection, and the macro average F1 values on Chinese and English datasets reach 77.3% and 94.7% respectively.