An improved algorithm for estimating the plane-fitting optical flow is proposed to further improve the accuracy of event sensor optical flow estimation and solve the fitting model error of the plane-fitting algorithm. The algorithm adopts the principle of the Prim greedy algorithm to extract effective events from the event flow and obtain the optimal local adjacent event set, laying the foundation for subsequent optical flow estimation. In addition, the eigenvalue algorithm is used to replace the traditional least square method, and the data ranking of the event set under the greedy algorithm is combined to optimize the plane-fitting model and improve the accuracy of the optical flow estimation algorithm. The experimental results show that compared with the existing estimation algorithm for event sensor optical flow based on plane-fitting, the algorithm improves the average endpoint error and average angle error by 20% and 11%, respectively. This study demonstrates effective improvement of the accuracy of the event sensor optical flow estimation algorithm.