Unlike traditional research results, the potential intelligence in data of funded projects is more strategic and forward-looking. This paper used text mining methods to analyze the data of funded projects in the field of deep learning between China and the United States, from the funding intensity, development trend, topic clustering, and evolution, and compare the similarities and differences of research hotspots in the field of deep learning between two countries, hoping to provide a reference for future scientific research layout in China. This study found that the number of fund projects in the field of in-depth learning in China and the United States has increased since 2010, with a good development momentum, and both countries have issued policies to support the development. However, China and the United States focus on different research directions. The United States focuses on the basic theory and algorithm research of deep learning, while China pays more attention to implementing application levels in deep learning. On the application level, the United States spends more attention to biomedical, economic, image recognition and hardware equipment applications. China pays attention to the biomedical field and uses deep learning related algorithms to apply more data in the geoscience and multimedia fields. In the emerging research direction, the research on hardware equipment and the application of deep learning has become a hot topic in the United States in recent years, while the new research focus in China is to apply deep learning to bioinformatics. It was founded that China should increase the investment of research funds to support the innovative development of AI in the future. To give full play to the scientific and technological layout advantages of the application in the field of geosciences, image and computer vision, multimedia, and traditional Chinese medicine, to form a new ecology of achievement transformation; To pay attention to the research of deep learning theory, improve the technical strength and grasp the initiative of science and technology; To make scientific plans in software, hardware and application, to construct the application of deep learning framework as soon as possible; To pay attention to the application of deep learning in the field of biological information, to promote the paradigm shift of biological information research, and release the massive potential of deep learning.
