Existing robust estimation methods of the fundamental matrix possess some limitations such as low accuracy. This study presents a fundamental matrix estimation method that uses multi-kernel learning to improve density peak clustering. First, from the viewpoint of the shortcomings in the density peak algorithm, such as the need to select parameters and inability to automatically cluster, multi-kernel learning and γ distribution map are introduced. Second, with the feature of epipolar distance, the proposed method eliminates the anomaly of the matching dataset to obtain a better internal point set. Finally, the M estimation method is used to exclude the positioning noise error, conduct further optimization processing on the internal idea set, and estimate the final base matrix. The INRIA dataset is used to validate and analyze the proposed method. Results show that the calculation accuracy and correctness of the fundamental matrix are improved using the proposed method.