Fault detection of shipboard antenna is of great significance to ensure the safe operation and smooth completion of astronautic measurement ship. With the development of data-oriented technology, intelligent fault detection is desiderated to improve self-management of entire shipboard antenna system. However, insufficient fault data results in intelligent algorithms stagnation. In this paper, a meta-learning network is specially designed for fault identification of shipboard antenna under small samples prerequisite, which is named affiliation network (AN). The AN consists of a random sampler, a feature extractor, an auxiliary classifier and a discriminator. The former three are utilized to extract and concatenate the features from training and testing samples, while the latter trains an adaptive pseudo-distance to evaluate the affiliation degree between concatenation features for identifying unknown data. Besides, a prior sufficient meta-training strategy is specially designed to realize metric-based knowledge transfer for acquiring the more generic AN, thus avoiding reiterative training of the AN in different application scenarios. Effectiveness of proposed method are validated by three experimental cases. Results indicate that, comparing with conventional intelligent models, the prior trained AN only utilized few samples to effectively identify failure categories of shipboard antenna even with complex operating conditions.