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The Bayesian vector autoregressive (BVAR) model introduces the statistical properties of variables as the prior distribution of the parameters into the traditional vector autoregressive (VAR) model, which can overcome the problem of too little freedom. The BVAR model established in this paper can overcome the problem of short time series data by using prior statistical information. In theory, it should have a good effect in China’s regional economic forecasting. Most regional forecasting model literature lacks out-of-sample forecasting error evaluation research in the real sense, but our early forecasts of major economic indicators provide an excellent opportunity for this paper to evaluate the actual forecast errors of the BVAR model in detail. The analysis in this paper shows that the prediction error of the BVAR model is very small and the prediction ability is very satisfactory. At the same time, this article also analyzes and points out the direction of efforts to further improve the prediction accuracy of the BVAR model.

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With the rapid development of science and technology, the application of computer vision is more and more widely, and there are great changes in all walks of life. New retail can rely on this advantage to improve the intelligent settlement of supermarket and reduce the workload of supermarket staff. In this paper, we use deep learning to train the goods that need to be settled, and identify the goods in the video during the settlement. At the settlement interface, Alipay two-dimensional code is generated to sweep the code for customers. QT is used to display the process of commodity identification and settlement.

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