Economic Behavior Modeling and Market Optimization Strategies Based on Game Theory and Deep Learning

Grasping economic behavior is a foothold of market optimization, this paper combines game theory ideas with deep learning technology to explore the laws of economic behavior. Firstly, different payment strategies are considered and payment matrices are constructed, and replicated dynamic equations are used to describe the dynamic adjustment process of the game and simulate the game process of market economic transactions. The MS-RCNN model that can be used to predict economic behavior is constructed by extracting data features using CNN and processing the feature matrix using GRU. The results of the game simulation simulation show that when the government regulation of the market is in place, it is easier for the trading parties to reach a deal. In addition the MS-RCNN model can more accurately reflect the fluctuation of the market when making long-term and short-term predictions, and the predicted price is closer to the real market price. Therefore a better understanding of economic behavior through game theory and its prediction through deep learning helps to achieve the optimization of market strategies.