In real-world data analysis, researchers often need to use multiple metrics at the same time. When modeling data, there is often strong collinearity between these indicators. If all indicators are directly included in statistical analysis or model construction, it will lead to problems such as model instability and difficult explanation of results. If some indicators are removed directly, a lot of information will be lost. principal component analysis (PCA) can be used to solve such problems.