In order to solve the problems of "hard classification" clustering, data sparsity and scalability in user-based or item-based Collaborative Filtering Recommendation (CFR) algorithms, a distributed collaborative filtering recommendation algorithm based on gray association analysis was proposed. Based on Hadoop platform, the grey relational coefficient of each item in rating matrix was calculated at first, then the Grey Relational Grade (GRG) of each item was calculated. Finally, the similar items for each item was constructed according to GRG, and item's rating for different users with related similar items was predicted. The experiment was conducted on the MovieLens dataset. The results showed that the Mean Absolute Error (MAE) of proposed algorithm was reduced by 1.07% and 0.06% respectively compared to the user-based and item-based CFR algorithms; and with the scale of dataset expending, the running efficiency was also improved by adding datanode to the Hadoop cluster. The experimental results illustrate that the proposed algorithm can make effective recommendation for large scale dataset and solve the problem of data scalability.