During the grid fault recovery stage, renewable energy converters may repeatedly enter and exit the low voltage ride through (LVRT) control mode, leading to increasingly prominent voltage oscillations induced by switching-type control. This phenomenon exhibits distinct discrete and nonlinear characteristics, making it difficult to accurately characterize the multi-parameter feasible region using conventional analytical methods. To address this issue, this paper proposes a multi-parameter feasible region calculation method based on non-iterative kernel learning, aiming to resolve the voltage oscillation problem induced by repeated LVRT transitions. First, a grid-connected converter system model incorporating LVRT control is established. On this basis, a comparative analysis is conducted to reveal the essential differences in mechanisms between voltage oscillations dominated by switching-type control and small-disturbance stability issues. Then, the point-biserial correlation coefficient method is employed to quantify the relationship between system parameters and repeated LVRT behavior, thereby identifying the key parameters affecting system stability. Finally, a non-iterative kernel learning-based classification model is constructed to achieve accurate identification of repeated LVRT phenomena and efficient characterization of the feasible region of key parameters. Time-domain simulation results demonstrate that the proposed method achieves high accuracy and effectiveness in feasible region computation, providing a new solution for analyzing and mitigating the repeated LVRT problem of renewable energy converters.