Traditional collaborative filtering techniques deal with no time-effect data. For recommender systems, as user preferences change over time, it can make the preferences of recommendation results change. In addition, the data explosion makes the model process more and more data over time, which greatly increases the computational cost. To solve the above problems, a similarity index time-effect collaborative filtering algorithm based on Attentional Double BP Network (SITCF-ADBP) is proposed. Firstly, three time-effect functions are proposed for processing evaluation metrics of user preferences changed over time. Secondly, after clustering the training samples using K-Nearest Neighbor (KNN), a set of similarity index screening principles is specified, and only one sample with high similarity is retained to reduce the input of the model. Finally, a Double BP Network incorporating a channel attention mechanism is designed to process the adequately processed data. Experiments were conducted on the MovieLens datasets (ml-1m) and (ml-100k), wherein our proposed approach was benchmarked against several state-of-the-art (SOTA) deep learning methods. The obtained results indicate that the model can effectively cope with the time-effect and data explosion of the recommender system.