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In order to solve the problem of feature point matching in lane detection with low real-time accuracy and low accuracy, this paper first proposes an improved Hough transform based on the vanishing point to extract the characteristic line, eliminates the interference line and improves the computational complexity. K-means clustering and RANSAC fitting algorithm were used in the dataset. Firstly, K-means clustering was used to preprocess the feature points extracted from the improved Hough transform, and the isolated feature points were removed, and then matched with Catmull-Rom spline curve RANSAC fitting, which is equivalent to quadratic optimization, enables fast and accurate registration of lane lines. Experiments show that this algorithm not only improves the accuracy of lane line recognition, but also has good robustness.