Abstract: With the formation of the "double high" power system which is connected to high proportions of new energy and power electronic equipments, the oscillations problem with characteristics of wide frequency domain, strong time-varying, strong nonlinearity, multi-modality and wide area propagation has become increasingly complex. At present, there is still a lack of unified and effective mathematical models and analysis methods. Artificial intelligence (AI), due to its low dependence on system models, strong learning capabilities for nonlinear and complex relationships between large amounts of data, and rapid adaptability to time-varying environments, is helpful in solving wide-band oscillations problems. In this paper, the feasibility and advantages of using AI to solve wide-band oscillations problems were analyzed based on the characteristics of models, analysis methods and manifestations. Then, the research results of AI applied to wide-band oscillations were analyzed and refined from three aspects: identification, location and suppression, and the typical framework was abstracted. On this basis, the challenges faced by AI in the above three areas were discussed, such as sample completeness, method transferability and robustness, and algorithm convergence in wide-area interconnected systems. Finally, combined with the latest development of AI and the research dynamics of wide-band oscillations, some future research ideas of AI in wide-band oscillations were pointed out from the perspectives of sample acquisition, algorithm interpretability and its integration with wide-band oscillations characteristics.