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In this paper, the fuzzy artificial neural network (ANN) is briefly described, and the fuzzy artificial neural network is used in the risk prediction of the project construction. The risk prediction of the construction project in the construction section is studied from four parts. Introduction Construction projects generally have a long time-consuming, investment ratio, the impact of many factors, from the engineering construction project engineering design, engineering construction, project decision-making, project completion and acceptance of the entire process, need to analyze from a scientific point of view, This paper studies the risk prediction of the construction project in the construction section by combining the fuzzy theory with the neural network [1], which is based on the interaction between the historical project construction project and the future development direction of the construction project. Combined with a project example, based on the fuzzy artificial neural network engineering construction project in the construction section of the risk prediction in-depth study for the construction project to provide a scientific risk forecasting method, establish a sound risk early warning system, and effectively reduce the construction project in the construction process In the presence of security risks and quality risks. The Fuzzy Artificial Neural Network Model Fuzzy artificial neural network model is a combination of fuzzy theory and artificial neural network theory of a comprehensive theoretical knowledge, with identification, association, learning, information processing and other functions. According to the risk early warning system of the construction project in the construction project, the fuzzy artificial neural network is used to construct the mathematical model, and the information is distributed. The model is divided into four levels. Generally, the artificial neural network system and the fuzzy system a method of series connection is expressed by the nodes of the artificial neural network system as fuzzy system signals, and the nodes implied by the artificial neural network system represent the fuzzy rules [2]. The fuzzy artificial neural network model is the input layer, the fuzzy layer, the fuzzy reasoning layer and the output layer, and the input layer mainly uses the number of nodes as the input variable number, which is an exact value. The fuzzy Layer is fuzzy on the input variables; fuzzy reasoning is the artificial neural network fuzzy reasoning; output layer is the number of nodes as the number of output variables. To improve the accuracy of the model, it is necessary to clarify the weight of the index according to the analytic hierarchy process (AHP), to make the index weight as a model input index and to fuzzify the data. The data is regarded as the fuzzy reasoning layer input in order to achieve from the input variable to the output variable fuzzy value mapping. The Risk Prediction Method in Construction Section of Construction Project In the construction project of the construction project risk prediction, the two methods are mainly used, one is fuzzy artificial neural network method and the other is the investigation and expert scoring method. The essence of the fuzzy artificial neural network is to distribute the information in 263 5th International Conference on Frontiers of Manufacturing Science and Measuring Technology (FMSMT 2017) Advances in Engineering Research (AER), volume 130

Research on Risk Prediction of Construction Section in Construction Project Based on Fuzzy Artificial Neural Network