<p>In many intensively managed fisheries, hatchery stocks are initially derived from wild populations, and even after multiple generations of aquaculture breeding, the degree of divergence is often insufficient to result in clearly defined genetic boundaries. Under such conditions, large-scale stock enhancement and frequent escape of cultured individuals create substantial challenges for fisheries assessment by obscuring individual origin in natural catches. Here, we present a streamlined genetic framework for identifying hatchery-origin individuals within natural-catch <i>Larimichthys crocea</i>, a species that has experienced extensive aquaculture development and long-term enhancement releases. The framework integrates a small panel of highly informative SNPs with a multilocus, genotype-based classification strategy that explicitly accounts for incomplete genetic differentiation. Individuals are grouped into high-confidence hatchery-origin, high-confidence wild, and an intermediate genotype class with quantified residual uncertainty, which can be further refined using mitochondrial haplotypes. Validation using individuals of known genetic background demonstrated full concordance for high-confidence genotype classes, while intermediate genotypes showed partial overlap between hatchery and wild backgrounds. Because intermediate genotypes occurred at low frequency in natural populations, the expected misclassification probability for randomly sampled individuals was therefore approximately 0.46%, reflecting both the low frequency of intermediate genotypes and their quantified residual uncertainty. Rather than relying on fully diagnostic loci, this framework emphasizes interpretability, cost efficiency, and suitability for routine fisheries monitoring. Although illustrated using <i>L. crocea</i> as a case study, the approach is conceptually general and applicable to fisheries species with recent domestication histories, extensive stock enhancement, or frequent escape of cultured individuals.</p>