Abstract: The growth of big data and artificial intelligence technologies has driven the rapid development of digital intelligence fluid mechanics. Digital intelligence fluid mechanics combines fluid mechanics, big data and artificial intelligence, to establish a new research paradigm oriented to specific scenarios of fluid mechanics, with "data" as the basis, "intelligence" as the core, and arithmetic power as the support. Its connotation is to establish a "data + physics" co-driven digital intelligence model, which is mainly data-driven and incorporates prior knowledge such as physical information and expert experience, to solve practical problems in different scenarios. Digital intelligence fluid dynamics has very obvious advantages in modeling flexibility, computing efficiency, and computational accuracy, whose application potential has been proven in multi-scale flow, multi-field coupling, and flow field modeling. In terms of the construction of digital intelligence models, data governance is indispensable since the data quality improved by governance enables intelligent algorithms to perform preferably. There are four main mechanisms for introducing "data + physics" co-driving in intelligent algorithms, which are input data-based embedding mechanism, model architecture-based embedding mechanism, loss function-based embedding mechanism and model optimization-based embedding mechanism. Taking oil & gas field applications as an example, a series of research advances in the prediction of physical parameters, evaluation of fracturing effects and optimization of injection parameters by digital intelligence fluid dynamics are introduced. Future diversified research models can take advantage of the efficient and rapid modeling of digital intelligence fluid dynamics, but also ensure physical interpretability and extrapolation in both classical and computational fluid dynamics. Therefore, digital intelligence fluid mechanics is an inevitable trend in the future development of fluid mechanics, and it is necessary to take the scenario demand as the guide, deeply integrate physical information and prior knowledge, actively explore new intelligent theories and methods, and attack the complex and changing scientific problems in fluid mechanics.