Aiming at the weaknesses of sparse data, low scalability and large computing existing in the current collaborative filtering algorithm, a BlockClust-Alternating least squares with Weighted regularization (BC-AW) collaborative filtering recommendation algorithm is proposed. Firstly, the user and the item of the original scoring matrix are jointly clustered and several submatrixes with the same scoring mode are generated. According to the research, the scale of these submatrixes is far less than the original scoring matrix which effectively decreases the computational complexity in the prediction process. Then, the regularized iterative least-square method is applied to each submatrix to predict its score. Hence recommendation is realized. The simulation results reveal that the proposed algorithm can effectively improve sparsity, expand scalability, and reduce computing compared with the traditional one.