In recent years, big data has increasingly drawn the attention of the R&D community. With the advent of marine data, monitoring marine big data becomes a new trend that advocates for assessing human impact on marine data. Nevertheless, there is a lack of support for acoustic sounds classification in such environment, covering diverse data that can exist (i.e., fish sounds, human activities sounds and environmental sounds). In this paper, we cope with this gap by proposing a deep learning-based approach that enables to efficiently classify these acoustic sounds aiming at automating the support of marine sound analysis in big data architectures. A set of experiments have been conducted using a real marine dataset to demonstrate the feasibility and the effectiveness of our approach.