Research on Enterprise Innovation Interaction Model Based on Complex Network and Big Data Analysis

This paper proposes an enterprise innovation interaction model based on complex network and big data analysis. The model uses PageRank algorithm to analyze the importance of each node in the enterprise innovation network, identify the core enterprises in the innovation activities and their key roles in the cooperation network. By constructing a complex network, the cooperation relationship in the enterprise innovation system is visualized, and valuable interaction patterns are extracted by combining big data analysis. The system design includes modules such as data preprocessing, complex network construction and PageRank algorithm implementation, which can effectively improve the accuracy of enterprise innovation interaction. During the simulation process, the paper used a real data set containing 500 enterprises and 2000 cooperation projects. The results show that the system can significantly improve the efficiency of identifying key nodes in the cooperation network between enterprises. The node importance identification accuracy of the PageRank algorithm reaches 93.2%, which is about 10% higher than the traditional method. In addition, the simulation results show that the prediction accuracy of core node identification and innovation interaction patterns in complex networks has also been significantly improved.