Consumption intent, defined as the decision-driven force of consumption behaviors, is crucial for improving the explainability and performance of user-modeling systems, with various downstream applications like recommendation and targeted marketing. However, consumption intent is implicit, and only a few known intents have been explored from the user consumption data in Meituan. Hence, discovering new consumption intents is a crucial but challenging task, which suffers from two critical challenges: 1) how to encode the consumption intent related to multiple aspects of preferences, and 2) how to discover the new intents with only a few known ones. In Meituan, we designed the AutoIntent system, consisting of the disentangled intent encoder and intent discovery decoder, to address the above challenges. Specifically, for the disentangled intent encoder, we construct three groups of dual hypergraphs to capture the high-order relations under the three aspects of preferences and then utilize the designed hypergraph neural networks to extract disentangled intent features. For the intent discovery decoder, we propose to build intent-pair pseudo labels based on the denoised feature similarities to transfer knowledge from known intents to new ones. Extensive offline evaluations verify that AutoIntent can effectively discover unknown consumption intents. Moreover, we deploy AutoIntent in the recommendation engine of the Meituan APP, and the further online evaluation verifies its effectiveness.