My research focuses on the cutting-edge domains of the next-generation artificial intelligence technologies. I mainly study efficient methods for processing high-dimensional data based on low-rank models. My work is dedicated to addressing prevalent issues in the big data industry, such as complex data structures, strong noise, damaged or missing data, thereby overcoming the limitations of traditional machine learning methods. Throughout years of research, I have employed non-convex optimization methods based on low-rank three-dimensional arrays, successfully tackling fundamental challenges such as multi-view data subspace clustering, high-dimensional data completion, three-dimensional image salient region detection, and video foreground-background separation. My research achievements have made a significant impact both domestically and internationally, receiving high praise from peers in the field. Pertinent research results have been published in authoritative international journals such as IEEE T-SP (1 as the first author), SIAM Journal on Imaging Sciences (1 as the first author), and Pattern Recognition (1 as the first author and 1 as the corresponding author), covering areas like pattern recognition and machine learning. Additionally, I have collaborated with several co-authors to publish over 10 papers in JCR Q1 journals, including Neural Networks, Information Sciences, IEEE Transactions on Cybernetics (T-CYB), T-MM, T-KDE, T-IP, among others. These works have been cited nearly 200 times on Google Scholar. My research has been positively recognized and cited by scholars from countries including China, the United States, and Belgium.