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As construction Industry is part of the complex non-linear system of the national economic development, the BP neural network of the artificial intelligence can to some extent deal with problems of the complex nonlinear system. Therefore, it has been widely applied to solving macroeconomic problems at present. The paper through systematically integrating econometrics with BP neural network establishes a complex nonlinear system predictive model for economic problems which is based on causality theories to determine the input variable of BP neural network, on the momentum BP algorithm of alterable learn rate and coitegration theories to analyze the reliability of BP neural network system. Therefore, the paper reinforces the theoretical basis, improves the quality of the network model and applies the predictive model to predicting and controlling the output of northwest construction industry of China, which has obtained satisfactory results.