The traditional collaborative filtering recommendation algorithm based on neighborhood has problems of data sparsity and similarity measures only utilizing ratings of co-rated items, so a Collaborative Filtering algorithm based on Bhattacharyya coefficient and Jaccard coefficient (CFBJ) was proposed. The similarity was measured by introducing Bhattacharyya coefficient and Jaccard coefficient. Bhattacharyya coefficient could utilize all ratings made by a pair of users to get rid of common rating restrictions. Jaccard coefficient could increase the proportion of common items in similarity measurement. The nearest neighborhood was selected by improving the accuracy of item similarity and the preference prediction and personalized recommendation of the active users were optimized. The experimental results show that the proposed algorithm has smaller error and higher classification accuracy than algorithms of Mean Jaccard Difference (MJD), Pearson Correlation (PC), Jaccard and Mean Squared Different (JMSD) and PIP (Proximity-Impact-Popularity). It effectively alleviates the data sparsity problem and enhances the accuracy of recommendation system.