Research on New Business Data Analysis and Visualization Technology Assisted by Artificial Intelligence

This study introduces a novel approach to business data analysis and visualization, leveraging artificial intelligence techniques, particularly the t-distributed stochastic neighbor embedding (t-SNE) algorithm for dimensionality reduction and the graphical representation of high-dimensional datasets. The proposed system integrates a variety of algorithmic frameworks, including principal component analysis (PCA) and various clustering algorithms, aiming to enhance both the efficiency and precision of business data interpretation. Through extensive simulation tests conducted on large-scale business data sets, the t-SNE algorithm effectively transforms intricate high-dimensional information into a two-dimensional format, providing a clear visualization of data clusters and inherent patterns. The experimental outcomes reveal that the visualization platform utilizing t-SNE significantly enhances the depth of data interpretation, particularly when handling multidimensional data, demonstrating superior accuracy and resilience. Compared to conventional visualization methods, this system exhibits approximately a 15% increase in accuracy during large-scale business data processing, offering substantial support for corporate decision-making processes. This research presents innovative intelligent tools for business data analysis, further advancing the data-driven decision-making framework in the corporate domain.