Using Machine Learning Methods to Predict Consumer Confidence from Search Engine Data

The consumer confidence index is a leading indicator of regional socioeconomic development. Forecasting research on it helps to grasp the future economic trends and consumption development in advance. data contained Internet era big can truly timely reflect current trends. This paper constructs conceptual framework for relationship between web search keywords. It employed six machine learning deep models: BP neural network, convolutional support vector regression, random forest, ELMAN extreme predict index. study shows that use models has better prediction effect Compared with other models, network have lower error indicators higher model accuracy, which decision-makers forecast Consumers various goods prices, as well macroeconomics, understand conditions market, affects decisions. Therefore, be used confidence. Future extended macro indicator-related studies. important promote market confidence, improve policies, national prosperity.