Design of Composite Factor Variance and Component Analysis System Based on Data Analysis Algorithm

It is very difficult to analyze and mine massive and high-dimensional data. Although the current hardware conditions have greatly improved, they still cannot meet the needs of large-scale, high-dimensional data. In medical research, scientific experiments, and biological gene sequencing, a combination of analysis of variance and principal component analysis was used to study the above issues. On this basis, this article started from two main data analysis methods and studied the relevant theories and algorithms of binary analysis of variance and principal component analysis, in order to achieve the analysis of sample data. The experiment proved that the data analysis based on the system in this article can combine the factor load matrix of the main factor variables to evaluate the contribution of each factor in the investment portfolio to the overall risk. Finally, the optimal stock portfolio was 601989, 601601, 600028, 600104, 601857, and 601988.