Abstract: With the development of high performance computer and data sharing platform, a large number of high fidelity turbulence data can be obtained. Recently, due to the evolution of artificial intelligence, like deep neural network, data-driven machine learning methods have been adopted to quantify the model uncertainty and improve and construct turbulence models. The combination of big turbulence data and artificial intelligence becomes a new area of turbulence research. Although some encouraging results have been achieved, there are still many difficulties and challenges, such as the generalization ability and robustness of the models, etc. The modeling process involves various aspects including data process, feature selection and selection and optimization of the model framework, etc. This paper analyzes and summarizes the main research progress from two aspects:the implementation methods of machine learning in turbulence modeling and the different model targets. Besides, the challenges and future works in this area are also discussed.