Abstract：

The stable operation of macroeconomics is crucial for the economic development of countries and the world. Accurate macroeconomic forecasts can provide strong support for the government to formulate fiscal policies, monetary policies, and industrial policies, help enterprises plan production, investment, and market expansion strategies reasonably, and also provide key references for investors in asset allocation and risk management. Traditional macroeconomic forecasting methods have limitations when facing complex economic environments, and machine learning algorithms bring new opportunities to this field. Based on this, the study first deeply analyzes the principles of common machine learning algorithms, then comprehensively compares the applicability of various machine learning algorithms in macroeconomic forecasting through comparative experiments, and finally proposes optimization strategies based on the characteristics of macroeconomic data and forecasting task requirements, aiming to improve the performance of machine learning algorithms in macroeconomic forecasting and provide more accurate basis for economic decision-making.

talents.

Key Words：

outcome-oriented education; computer application talents; innovation training; educational model