Content resource popularity prediction is one of the main basis for content delivery network to improve the efficiency of caching and scheduling. In view of the poor feature representation ability, adaptability and low accuracy of current popularity prediction algorithms, this paper proposes a content resource popularity prediction algorithm based on deep learning. The algorithm is better based on a two-way GRU model of the fusion attention mechanism, which can better mine the information contained in the resource access history and its correlation, improve the efficiency and quality of feature extraction, and has a more tolerant generalization ability. The experimental results on different data sets show that the various indicators of the algorithm are better than the existing mainstream algorithms, and the accuracy rates are as high as 96.20% and 98.03%.