  With the widespread engineering applications ranging from artificial
intelligence and big data decision-making, originally a lot of tedious
financial data processing, processing and analysis have become more and more
convenient and effective. This paper aims to improve the accuracy of stock
price forecasting. It improves the support vector machine regression algorithm
by using grey correlation analysis (GCA) and improves the accuracy of stock
prediction. This article first divides the factors affecting the stock price
movement into behavioral factors and technical factors. The behavioral factors
mainly include weather indicators and emotional indicators. The technical
factors mainly include the daily closing data and the HS 300 Index, and then
measure relation through the method of grey correlation analysis. The
relationship between the stock price and its impact factors during the trading
day, and this relationship is transformed into the characteristic weight of
each impact factor. The weight of the impact factors of all trading days is
weighted by the feature weight, and finally the support vector regression (SVR)
is used. The forecast of the revised stock trading data was compared based on
the forecast results of technical indicators (MSE, MAE, SCC, and DS) and
unmodified transaction data, and it was found that the forecast results were
significantly improved.
