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After deep learning algorithms were proposed, artificial intelligence technology applications have achieved breakthrough development. Currently, the explosive growth of data provides sufficient nutrients for artificial intelligence, and deep learning algorithms have achieved breakthroughs in speech and visual recognition, making it possible for artificial intelligence industries to land and commercialize. With the continuous development of big data, deep learning and cloud computing, we can obtain more and richer data, develop more efficient algorithms, and have more powerful computing power, laying the foundation for another artificial intelligence research boom. However, with the application of AI-related technologies in industries such as information, social governance, and transportation, the problems and challenges of algorithmic collusion and algorithmic discrimination have gradually emerged. The operating principles of algorithms differ from the risk of algorithmic collusion that may result, and they also pose different degrees of regulatory challenges for antitrust enforcement. By understanding the data-driven competitive model of the market under the influence of algorithms, the efficient information interaction mechanism and the new features embodied in the evolution of machine-driven competition can prevent the breeding of technological monopolies. The impact of algorithms on the collusion problem has two dimensions: the first dimension is to change the environment of collusion; the second dimension is to be applied directly as a tool in the collusion process. This article attempts to analyze the challenges brought to modern market competition by exploring the state of artificial intelligence technology that causes data monopoly. Thus, In order to better regulate the evaluation and regulation of artificial intelligence algorithm collusion, this article proposes related solutions based on the Chinese perspective, including: 1) broaden the extension of the competitive relationship and identify the subject of monopolistic behavior in accordance with the idea of cooperative behavior; 2) increase platform factors to identify market dominance; 3) use case analysis as the main method to define relevant markets.
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