Multi-Intention (MI) recognition of short texts is a problem in Spoken Language Understanding (SLU). The effective features of short texts are difficult to extract in classification problems because of sparse features of short texts and few words containing many information. To solve the problem, by combining syntactic features and Convolution Neural Network (CNN), a multi-intention recognition model was proposed. Firstly, the sentence was syntactically analyzed to determine whether it contains multi-intention. Secondly, the number of intentions and matrix of distance were calculated by using Term Frequency-Inverse Document Frequency (TF-IDF) and word embedding. Then the matrix of distance was acted as the input of CNN model to classify intentions. Finally, the emotional polarity of each intention was judged to return to the user's true intentions. The experiment was carried out by using the real data of the existing intelligent customer service system. The experimental results show that, the single classification precision of the combination model of syntactic features and CNN is 93.5% in 10 intentions, which is 1.4 percentage points higher than the original CNN model without syntactic features. And in mutil-intention recognition, the classification precision is 30 percentage points higher than others.