Abstract: To address the issues of high computational cost in Monte Carlo simulation, insufficient convergence efficiency of the AK-MCS method when dealing with complex problems, and the limited capability of traditional learning functions, this paper proposes a structural reliability analysis method based on adaptive ensemble Kriging. The proposed method first employs a high-density initial sampling strategy with space-filling mechanisms, incorporating local void detection and region-filling schemes to enhance the overall coverage of the design space and improve the accuracy of the initial Kriging surrogate model. Furthermore, a novel L-learning function is constructed by integrating the probability of prediction error, sample spatial distance, and a dynamic exploration factor. Meanwhile, an ensemble surrogate model is established, in which fusion weights are dynamically computed through leave-one-out cross-validation errors, integrating the prediction results of multiple sub-models to enhance the accuracy and stability of reliability analysis. The effectiveness of the proposed method is validated through reliability analysis of two case studies—a four-failure-domain series system and a six-dimensional nonlinear oscillatory system—and compared with five other reliability analysis algorithms. The results demonstrate the efficacy of the proposed method, providing an effective solution for structural reliability assessment of complex systems.