Research on Efficiency Coupling Coordination Feature Model of Digital Economy Based on Multi-Objective Machine Learning Algorithm

Based on the coupled coordination development theory, the evaluation feature system of the coupled coordination system is first established according to the principle of feature selection, and the feature system of the digital economy subsystem is constructed from the three dimensions of digital infrastructure, digital application development and digital development environment, and its weight is determined by comprehensive evaluation method and entropy method. Secondly, the original machine learning particle swarm optimization algorithm is improved, including the improvement of infrastructure, digital application development, and digital development environment. Then, the method is combined with the dynamic multi-objective technology, the clustering method is selected as the objective function, the background difference method is used to design the digital economy efficiency coupling coordination feature model and rules, and the dynamic multi-objective optimization diagnosis model of improved particle swarm is established to achieve the optimization of the digital economy efficiency coupling coordination feature model. Finally, through the grey correlation analysis method, the paper analyzes the specific factors and degrees that affect the coordinated development level of digital economy and real economy.