随着信息与智能技术的进步，低空经济近年来已成为理论研究与工程应用关注的热点之一。在支撑低空经济的众多理论与技术方法中，信息融合至关重要，但在复杂场景中依然面临信息异构性强、时空不统一、数据贫弱、协同互通难等严峻挑战。随着深度学习的迅猛发展，信息融合理论与方法也随之变化：融合的实现更加智能化、端到端、鲁棒与强自适应性，这也迎合了低空经济相关应用领域发展的急迫需求。本文将结合信息融合理论与方法在深度多模态学习时代的发展及其在低空经济领域中的应用开展研究，关注其优势、机遇特别是存在的挑战，探寻低空经济时代背景下的信息融合理论与方法研究的方向与路径。

With the advancement of information and intelligent technologies, the low-altitude economy has, in recent years, emerged as one of the focuses in both theoretical research and engineering applications. Among the various theoretical and technical approaches underpinning the low-altitude economy, information fusion plays a pivotal role. However, in complicated environments, it still encounter formidable challenges, including significant heterogeneity among information sources, lack of spatio-temporal consistency, data sparsity and dirty, and difficulties in cooperative interoperability. With the rapid development of deep learning, the theories and methods for multi-source information fusion have undergone significant advancements in recent years. These developments have enabled information fusion to become more intelligent, end-to-end, robust, and highly adaptive, which aligns well with the urgent needs of applications within the low-altitude economy. This paper aims to examine the evolution of multi-source information fusion theory in the era of deep multi-modal learning and its applications in the low-altitude economy, with a particular focus on its advantages, opportunities, and, especially, the remaining challenges. Furthermore, it seeks to explore the directions and pathways for the research of information fusion theories and methodologies in the context of the low-altitude economy.