To address the performance degradation of traditional Kalman filters in time-varying thick-tailed non-Gaussian noise environments, we propose a maximum correntropy Kalman filter (MCKF) algorithm based on distribution entropy(DE), called DE-MCKF. Among them, the DE is used to determine the confusion degree of measurement data errors, combined with the adaptive change strategy to optimize the MCKF under varied non-Gaussian noise while maintaining the filtering effect of the MCKF. Distributing the entropy can timely improve trust in the measurement value when a high entropy value causes over-change, avoiding over-reliance on model predictions and reducing the filtering errors from MCKF misjudgments on the noise. The results show that DE-MCKF reduces the root mean squared error (RMSE) of averaged latitude and longitude error by 62.76%, 58.46%, 55.11%, and 43.17% compared to KF, DEKF, MCKF and VBKF, respectively. It can effectively improve the positioning accuracy of the system under non-Gaussian noise.