A Data Visualization and Analysis Model for Multi-Information Fusion

Traditional data visualization and analysis methods based on network analysis can often only present information from a single source, and cannot fully understand and discover potential patterns and correlations in the data. To solve this problem, a data visualization analysis model of multi-information fusion is proposed, which aims to provide more comprehensive, accurate and insightful data understanding and decision support. Data mining techniques are then used to fuse multi-source data to provide a more comprehensive analytical perspective by discovering correlations and patterns between data. In terms of visual design, the model adopts an intuitive and interactive visual interface to display multidimensional data information to users in the form of charts and graphs. A series of experiments were designed and compared with traditional data visualization methods. The results indicate that users can flexibly explore and analyze data through operations such as selecting, filtering, and zooming, and uncover potential correlations and trends.