Calligraphy strokes have rich writer charateristics. Whether feature vectors can be correctly extracted and matched directly affect the recognition effect. Aiming at the problem that the traditional SURF（Scale Invariant Feature Transform）algorithm has fewer detected feature points and higher false matching rate, a SURF based on Contourlet transform is proposed. The algorithm uses Contourlet transform to do sub-band decomposition and directional filtering of calligraphic strokes before the feature points are extracted, and then obtains the low frequency and high frequency detail components. The minimum Euclidean distance criterion (LEDC) is adopted to calculate the similarity of the low-frequency detail components. After the high frequency detail components are further decomposed, the appropriate thresholds are selected to extract the high frequency feature points. Then, the SURF feature points are matched. The RANSAC algorithm is used to eliminate the false matching points. Experiments show that the improved SURF algorithm can not only extract the feature points of the strokes better, but also improve the anti-noise performance. The recognition rate is improved by 3%.