With the rapid development of e-commerce platforms and new digital media services, the scale of network data continues to grow and data types are diversified. The mining of valuable information from large-scale data has become a huge challenge for information technology. Recommendation systems can alleviate the “information overload” problem, explore the potential value of data, push personalized information to users in need, and improve information utilization. The combination of the representational capabilities of deep learning and recommendation systems helps to dig deeper into user needs and provide accurate personalized recommendation services. This study analyzes the advantages and disadvantages of traditional recommendation algorithms, summarizes the research progress of deep learning technology in recommendation systems, and probes into the future development directions of intelligent recommendation systems.