For online life service platforms, such as Meituan, user consumption intention, as the internal driving force of consumption behaviors, plays a significant role in understanding and predicting users' demand and purchase. However, user consumption intention prediction is quite challenging. Different from consumption behaviors, consumption intention is implicit and always not reflected by behavioral data. Moreover, it is affected by both user intrinsic preference and spatio-temporal context. To overcome these challenges, in Meituan, we design a real-world system consisting of two stages, intention detection and prediction. Specifically, at the intention-detection stage, we combine the knowledge of human experts and consumption information to obtain explicit intentions and match consumption with intentions based on user review data. At the intention-prediction stage, to collectively exploit the rich heterogeneous influencing factors, we design a graph neural network-based intention prediction model GRIP, which can capture user intrinsic preference and spatio-temporal context. Extensive offline evaluations demonstrate that our prediction model outperforms the best baseline by 10.26% and 33.28% for two metrics and online A/B tests on millions of users validate the effectiveness of our system.