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Top1. Introduction
Artificial intelligence (AI) technology has been used widely in the Internet field, and a database has become an important source of information. With the rapid development of computer applications, the development of information technology has brought people into an era of information explosion. Information technology has profoundly changed people's work and lifestyle, prompted changes in people's ideas, promoted scientific and technological progress, and accelerated industrial transformation. The development of computer software and application technology facilitates data processing. Data-driven data mining techniques have become a new field of research in AI, where the model used for data is not permanently related except for real-time data. The focus and complexity of information technology research have changed accordingly, and how to use this kind of big data in its valuable in-depth information and in-depth data analysis has become the focus of information research.
For information depth mining, Liu proposed a heuristic device to auto-determine the optimal number of screens to review. The experimental results of the simulation signals indicate the efficiency of the elastic signal proposed for the normal course of the LMD. Finally, the proposed method extracts configuration information for gear error analysis (Liu, Zuo, & Jin, 2017). Jin introduced information mining information from big data users to help design the product. He explained large-scale user data export research from multiple perspectives, such as data collection, location recognition, feature recognition and emotional analysis, thought collection, and sampling (Jin, Liu, & Ji, 2019). Wu proposed the application of world-class diversity equations to information mining based on user feedback. Hybrid mining algorithms avoid the weaknesses of an algorithm, which facilitates the identification and recognition of users' ideas efficiently and effectively (Wu, 2020). Qian proposed a basic approach to information abstraction based on the hazard matrix method, analyzed wheat flour processing, and applied it to practical mining ecosystem exploitation. Compared with the existing system, this approach can achieve the trajectory of the production process by combining different levels of raw materials, production levels, and final products from the system (Qian, Song, & Wang, 2019). As the amount of information increases, information mining cannot further meet people's needs. AI is hoped to address this problem.
AI has developed rapidly in recent years and has attracted the attention of many scholars. For AI applications, Hassabis explored the historical interaction between the fields of AI and science and highlighted current developments in AI. These developments are supported by research on nerve compression in humans and other animals (Hassabis, Kumaran, & Summerfield, 2017). Fei examined applications of AI in healthcare in detail. Its three main areas were early detection and detection, treatment and result prediction, and evaluation of prognosis. The paper discussed pioneering AI programs, such as IBM Watson, and barriers to the development of AI in real life (Fei, Yong, & Hui, 2017). Krittanawong referred to AI as a field of computer technology that aims to mimic human thought processes, training possibilities, and knowledge storage. The paper described the application of AI in cardiovascular therapy and discussed its possible role in developing the cardiovascular system (Krittanawong, Zhang, & Wang, 2017).