<p>Transfer learning has been successfully applied across multiple domains, aiming to uncover intrinsic relationships between data or models and transfer knowledge gained from source domain training to target domains. As a core method for evaluating technical efficiency, stochastic frontier models have consistently attracted significant attention. This paper innovatively combines the two approaches, proposing the Spatial Durbin Stochastic Frontier Model with an integrated transfer learning framework (Trans-SDF-STE). This model retains the Spatial Durbin model’s ability to capture “spatial correlation effects between an entity and its neighboring units” while extending the Stochastic Frontier Analysis’s advantage in quantifying heterogeneity in technical efficiency. Simultaneously, it overcomes modeling limitations in target domains with small samples through transfer learning mechanisms. We employ spatial residual bootstrapping to select source domains exhibiting “spatial structural consistency and efficiency mechanism similarity.” By integrating 2SLS estimation to construct instrumental variables addressing endogeneity, followed by a two-stage transfer learning approach (“transfer + bias removal”), we migrate the source domain to the target domain. This enhances the accuracy of model parameter estimation and technical efficiency measurement. Through simulation experiments and real-world data applications, we validate the effectiveness and practicality of the proposed method.</p>