A two-stage learning framework based on a long-range structure-enhanced masked autoencoder is developed for few-shot radar specific emitter identification (SEI). To improve the modeling of long-range fingerprint structures and the discriminative stability of embeddings under limited labeled samples, a self-supervised masked reconstruction task is first constructed for one-dimensional axially integrated bispectrum (AIB) sequences to learn structural priors from unlabeled data. A ResLKA-TS encoder, consisting of a residual backbone, large-kernel attention, and soft-threshold shrinkage, is then employed to enhance the representation of distributed fingerprint structures. In the few-shot fine-tuning stage, center loss is further introduced to improve intra-class compactness and inter-class separability in the embedding space. Experiments on a measured dataset collected from eight ADALM-PLUTO devices of the same model show that, at an SNR of 20 dB, identification accuracies of 88.00\% and 91.87\% are achieved under 10-shot and 15-shot settings, respectively, exceeding those of asymmetric masked autoencoder (AMAE) by 5.12 and 4.69 percentage points. The method also maintains advantages under low-shot and different SNR conditions.