The integrity of ocean observation data, gathered through various sources, is frequently marred by issues such as instrumental biases, extraneous interferences, and encoding errors. For the establishment of a centralized oceanographic big data hub, the rigorous quality control (QC) of these observations is both essential and critical to the reliability of subsequent oceanographic analyses and smart oceanographic applications. Current AI-driven QC methods, while promising, are often hindered by the need for substantial datasets, a requirement that is not met in many observational locations due to the paucity of historical data. This challenge necessitates the innovation of an intelligent QC model framework capable of reasoning in low- or no-sample contexts. Addressing this need, we have crafted a framework that leverages unsupervised pre-training on vast datasets, allowing for a data-driven discernment of the underlying patterns in ocean observation data. This framework is designed to provide high-precision predictive capabilities and robust QC for a diverse array of marine data types. This study marks the first instance of applying pre-trained modeling methodologies to the domain of marine data QC.