Scientific and accurate forecasts can not only help people to formulate correct work plans and action arrangements in a timely manner, but also provide important policy recommendations for economic development and promote stable and healthy economic development. In real life, everyone hopes to obtain very effective forecast results and provide necessary solutions for scientifically responding to future market changes. The main purpose of this paper is to analyze and study the prediction of consumer psychology in the digital economy based on DNNs. This paper establishes an analytical framework for the dynamic correlation characteristics of consumption and economic growth, and innovatively comprehensively evaluates the changes of the correlation between the two over time from three aspects: gravity coupling, cycle synergy and interactive influence. To compare the forecast results, the error function was used to calculate the error of the three forecast methods using the mean squared error. The experimental study shows that the DNN algorithm in this paper has the best prediction effect through the comparison charts and error comparison tables of three prediction results. The prediction error of the multiple linear regression algorithm is 0.483, the prediction error of the principal component regression algorithm is 0.214, and the prediction error of the DNN algorithm is 0.059, which is an order of magnitude higher than the previous two algorithms in accuracy.

Ruoxin Lin. Psychological Prediction of Digital Economy Consumption Considering Deep neural network. International Journal of Neural Network (2022), Vol. 3, Issue 3: 45-55. https://doi.org/10.38007/NN.2022.030306.

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