At present, the popularity of artificial intelligence has given people a profound understanding of it. Although many people do not fully understand the working principle of artificial intelligence, when it comes to artificial intelligence, well-known applications such as autonomous vehicle, intelligent interactive robots, robot dogs, unmanned aerial vehicles and so on come to people's minds. When artificial intelligence is deeply integrated into scientific research, what kind of paradigm shift will it bring and what new exploration spaces will it open up? Firstly, it is necessary to clarify the meaning of "scientific research paradigm". The research paradigm refers to the worldview and research methods commonly followed by the scientific research community, and is a universal principle for ensuring the efficient and orderly development of scientific research activities. The paradigm of human scientific research has undergone four important evolutions, namely the empirical paradigm, the theoretical paradigm, the simulation paradigm, and the data-driven paradigm. At different stages of scientific development, it is usually dominated by a certain paradigm. At the same time, as the limitations of the current paradigm gradually emerge that are difficult to explain new discoveries, the emergence of a new paradigm becomes inevitable. In current scientific research, especially in fields such as materials science, synthetic biology, chemistry, astronomy, and earth science, scientific data is showing explosive growth. In order to extract knowledge patterns from these massive amounts of data, traditional methods such as computer simulations and manual experiments often fail to meet expectations. For example, between 2005 and 2015, genomic sequence data almost doubled every seven months. In astronomy, the Hubble Space Telescope, which has been in operation since 1990, can transmit approximately 20GB of raw data per week. This is exactly the problem that researchers have long faced: firstly, the challenge of applying scientific research results in practice; Secondly, the efficiency of data collection, processing, and analysis is relatively low; Thirdly, most research teams still adopt a "workshop style" work mode, and platform based cooperation is relatively rare; The fourth is to rely on experience and trial and error to make breakthroughs in areas such as material research and development. These massive data require processing such as classification, regression, clustering, association analysis, time series analysis, and anomaly detection. Only after completing these steps will hidden patterns and unknown correlations emerge, otherwise it is just ineffective redundancy. At the same time, modern science has entered the era of complex systems, and traditional computational methods are unable to cope with the bottlenecks caused by the increasing number of variables and computational complexity. In this context, the core technology of artificial intelligence - deep learning - has demonstrated unique advantages. The design of deep learning originated from the demand for big data, and data processing is not only its strength, but also the foundation for its survival and development. Deep learning can identify patterns in large amounts of data and alleviate the challenges posed by data explosion. For example, repetitive experiments that are difficult for manual experimenters to complete in a day can be efficiently completed hundreds of times in a day through an automated platform, greatly improving the accuracy and consistency of experimental data. High quality experimental data is the foundation of simulation and training. The development of artificial intelligence technology has enabled scientists to surpass the traditional four research paradigms, relying on advanced computing technology to promote the fifth generation research paradigm - using artificial intelligence technology to learn, simulate, predict, and optimize natural phenomena, thereby promoting scientific discovery and technological innovation. Compared to traditional research methods, this research paradigm not only significantly improves the efficiency of solving scientific problems, but also provides researchers with new research perspectives and directions, opening up new paths for exploring the unknown. A typical example is that the 2024 Nobel Prize in Physics and Chemistry are both related to research in artificial intelligence. On the one hand, this affirms the crucial role of artificial intelligence in promoting the progress of basic science, and on the other hand, it also indicates that traditional disciplines such as physics and chemistry will become more open. Scientists will no longer be limited to the traditional "interpretability" research mode, but will continuously improve models through experimental calibration to obtain a more comprehensive understanding. Despite the many benefits brought by artificial intelligence, its application still needs to be approached with caution. For example, in biological research, both human individual information and medical feature information as research subjects contain a lot of privacy content. In the process of data mining and analysis, if data privacy is not effectively protected, it will to some extent affect the development of biology and the credibility of scientific research. Although some experts and scholars have proposed innovative technological means for data sharing and exchange, as well as model building and training, while ensuring data security, this issue still needs further exploration and resolution. (Author: Wang Zhongyou, Special Researcher at the Cyberspace Public Security Research Center of the Ministry of Industry and Information Technology) (News Agency)