Research on the Algorithm of Processing Related Data for Automatic Generation of Computerized Reports Using Complex Network Theory

This study focuses on the automatic generation of reports in a computerized environment and proposes an algorithm for processing related data based on complex network theory. By using the node and edge relationships in the complex network to construct a data association model, the dynamic association analysis of data in the report generation process is realized. The algorithm is designed based on the potential connection between data and relies on indicators such as degree centrality and clustering coefficient in the complex network to ensure the rationality and consistency of the data in the report. This paper comprehensively simulates the report generation and related data processing process by constructing a multi-level simulation model. The simulation results show that the proposed algorithm can meet the needs of automatic report generation in terms of accuracy and stability, and still show high robustness when processing data diversity and complexity. The algorithm is significantly superior to traditional methods in terms of association and processing efficiency. This study provides a novel technical idea for the automatic generation of computerized reports, and expands new possibilities for the application of complex network theory in the field of data processing.