Since the reform and opening up, China's real estate industry has been developing rapidly. The Chinese government has begun to carry out macro-control on real estate, planning and construction from various aspects including finance, people's livelihood and social security. Real estate enterprises, like the speed of the rocket into the sky, have gone bankrupt in the fierce market competition, and some lucky people choose merger and reorganization. Springing up, the continuous development of national economy, real estate enterprises to pour money into a variety of real estate enterprises, the reasonable management of real estate investment, identify risks, cultivate the spirit of safety, is a real estate enterprise top priority, reasonable research the risk of real estate enterprise case, well data and forward-looking consciousness. The experimental results show that the risk management efficiency of Real estate enterprises in China has been improved by 9.6% based on the research of risk management innovation mechanism under the macro background. The risk of real estate enterprises can be attributed to the following a few local governments for infrastructure, the construction is reasonable, is there any area to maximize utilization of land, urban road traffic conditions, the greening situation of living environment, reasonable collocation, green belts, improve housing, for building the height and size of the restrictions and management. If real estate enterprises do not understand these information and lead to unreasonable development and ultimately lead to financial crisis risk. For the financial risk analysis, discussion and summary, so as to the real estate risk prediction rich research results.

Li Ma. Risk Management Innovation Mechanism of China's Real Estate Enterprises under the Current Macro Background. International Journal of Frontiers in Sociology (2021), Vol. 3, Issue 15: 60-64. https://doi.org/10.25236/IJFS.2021.031507.

[1] Li Y , Tian S , Huang Y , et al. Driverless artificial intelligence framework for the identification of malignant pleural effusion[J]. Translational Oncology, 2021, 14(1):100896.

[2] Hassabis D , Kumaran D , Summerfield C , et al. Neuroscience-Inspired Artificial Intelligence[J]. Neuron, 2017, 95(2):245-258.

[3] Lu H, Li Y, Chen M, et al. Brain Intelligence: Go Beyond Artificial Intelligence [J]. Mobile Networks and Applications, 2017, 23(7553):368-375.

[4] Ng T K. New Interpretation of Extracurricular Activities via Social Networking Sites: A Case Study of Artificial Intelligence Learning at a Secondary School in Hong Kong [J]. Journal of Education and Training Studies, 2021, 9(1):49-60.

[5] Camerer C F. Artificial Intelligence and Behavioral Economics [J]. NBER Chapters, 2018, 24(18):867-71.

[6] Jia-zhan, Su, Xi-lun, et al. Full-scale bending test and parametric study on a 30-m span prestressed ultra-high performance concrete box girder:[J]. Advances in Structural Engineering, 2019, 23(7):1276-1289.

[7] Li Y, Yan L, Wang L , et al. Estimation of normal distribution parameters and its application to carbonation depth of concrete girder bridges[J]. Discrete & Continuous Dynamical Systems, 2019, 12(4&5):1091-1100.

[8] Zhang G, Wang J , Liu J , et al. Analysis of loss in flexural stiffness of in-service prestressed hollow plate beam[J]. International Journal of Structural Integrity, 2019, ahead-of-print (4):534-547.

[9] Needhi, Kotoky, Anjan, et al. Hybrid testing for evaluation of seismic performance of highway bridge with pier made of HyFRC - ScienceDirect [J]. Structures, 2019, 20:848-865.

[10] Yuanyao M , Ditao N . Effect of Vehicle Load on the Fatigue Performance of Corroded Highway Bridge [J]. Science of Advanced Materials, 2018, 10(6):845-852.