Research on the Fusion Path of Enterprise Big Data Technology Based on Ant Colony Algorithm

In view of the limitations of the traditional Grayscale trajectory algorithm in the fusion path of enterprise big data technology, a research scheme of fusion pathway study based on ant colony algorithm was proposed. Firstly, the influencing factors is accurately located through Group behavior theories, and the indicators is reasonably divided to reduce interference, and the ant colony algorithm is used to construct a research scheme for fusion pathway study. Experimental results show that under certain evaluation criteria, the proposed scheme is superior to the traditional Grayscale trajectory algorithm in terms of research accuracy and processing time of influencing factors of fusion pathway study, and has obvious advantages. The research of fusion pathway study plays an extremely important role in enterprise big data technology, which can accurately predict and optimize the growth characteristics and product generation of enterprise big data technology. However, the traditional Grayscale trajectory algorithm has some limitations in solving the problem of technology fusion simulation, especially when dealing with complex problems. In this paper, this paper proposes a research scheme of fusion pathway study based on ant colony algorithm to better solve this problem. The scheme accurately locates the influencing factors through Group behavior theories, so as to determine the division of indicators, and uses the ant colony algorithm to construct the scheme. Experimental results show that under certain evaluation criteria, the accuracy and speed of the scheme is significantly improved for different problems, and it has better performance. Therefore, the simulation scheme based on ant colony algorithm in the fusion path of enterprise big data technology can better solve the limitations of traditional Grayscale trajectory algorithm and improve the simulation accuracy and efficiency.