For the problem that when the user score is not enough, the recommender system significantly reduces the data sparse recommendation performance, a Collaborative In Deep Learning algorithm (CIDL) is proposed. The algorithm firstly conducts the deep learning on a large amount of data, and then performs collaborative filtering on the rating (feedback) matrix to arrive at a recommendation item for the user. This study uses real movie data to test and to compare it with the other four excellent algorithms. It is proved that CIDL can effectively solve the problem of reduced performance due to data sparseness and improve the accuracy of the recommendation.