Focusing on the problems of low feature extraction speed and low matching efficiency of the traditional image copy detection algorithm based on Scale Invariant Feature Transform (SIFT) feature, a fast image copy detection algorithm based on location distribution and orientation distribution features of SIFT feature points was proposed. Firstly, the two-dimensional location information of SIFT feature points was extracted. The number of feature points in each interval was counted with block statistics by calculating the distance and angle between each feature point and image center point. The binary hash sequence was generated to construct the first order robust feature according to the quantitative relationship. Then, the numbers of sub-interval feature points in all directions were counted with block statistics according to the one-dimensional direction distribution feature of feature points, and the secondary image feature was constructed according to the quantitative relationship. Finally, a cascade filter framework was used in the copy detection to make a judgement about whether the copy or not. The simulation experimental results show that, compared with the traditional copy detection algorithm which constructs the hash sequence based on the SIFT feature with 128-dimensional descriptor, the feature extraction time of the proposed algorithm is shortened to the original 1/20, and the matching time is also reduced by more than 1/2. Therefore the proposed algorithm meet the requirement of online copy detection.