Abstract
Financial market and economic growth and development trends can be regarded as an extremely complex system, and the in-depth study and prediction of this complex system has always been the focus of attention of economists and other scholars. Emotion recognition algorithm is a pattern recognition technology that integrates a number of emerging science and technology, and has good non-linear system fitting capabilities. However, using emotion recognition algorithm models to analyze and predict financial market and economic growth and development trends can yield more accurate prediction results. This article first gives a detailed introduction to the existing financial development and economic growth status and development trend forecasting problems, and then gives a brief overview of the concept of emotion recognition algorithms. Then, it describes the emotion recognition methods, including statistical emotion recognition methods, mixed emotion recognition methods, and emotion recognition methods based on knowledge technology, and conducts in-depth research on the three algorithm models of statistical emotion recognition methods, they are the support vector machine algorithm model, the artificial neural network algorithm model, and the long and short-term memory network algorithm model. Finally, these three algorithm models are applied to the financial market and economic growth and development trend prediction experiments. Experimental results show that the average absolute error of the three algorithms is below 25, which verifies that the emotion recognition algorithm has good operability and feasibility for the prediction of financial market and economic growth and development trends.
Highlights
Financial development is the core of economic growth, and its development trend has always been the focus of the government and countless market investors
This paper proposes a novel research direction of financial development and economic growth status and development trend based on emotion recognition algorithm, and provides economists with a data analysis platform based on intelligent science and technology
Three algorithm models of support vector machine (SVM), artificial neural network (ANN), and long and short-term memory network (LSTM) in the statistical emotion recognition method are selected to predict the operation of the financial market
Summary
Financial development is the core of economic growth, and its development trend has always been the focus of the government and countless market investors. These advanced algorithms can realize fast data collection and processing, and can accurately output analysis results They are extremely versatile, and applying them to the study of financial development and economic growth status and development trends can effectively solve the problem of complex factors interfering with prediction. This paper proposes a novel research direction of financial development and economic growth status and development trend based on emotion recognition algorithm, and provides economists with a data analysis platform based on intelligent science and technology. This platform can effectively make accurate judgments on the financial market and economic development, and can provide new ideas for research in the financial and economic fields
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