With the rapid development of 5G technology, there is a growing expectation for its applications in the communication field. One of the key performance indicators for 5G networks is handover quality, making its prediction a significant area of current research. This study aims to explore the use of deep learning models to accurately predict handover quality in 5G networks. By analyzing 5G network data, including signal strength, physical cell identity (PCI), and reference signal received power (RSRP), the study conducted power signal classification on relevant datasets. Subsequently, a GRU neural network model and a CNN model were employed to learn the complex relationships among these data, enabling accurate prediction of handover quality. The experimental results demonstrated that this approach could control the average error within 4% to improve 5G network performance and provide significant practical value in the establishment of intelligent 5G networks. Future work will focus on further exploring the practical applications of this model, with the goal of enhancing the service quality and user experience in 5G networks.