With rapid development of mobile edge computing， providing high-quality mobile services requires considering various factors that affect network communication diversifiedly according to the real-time changes of user mobility trajectories， and service migration paths should be dynamically planned. Addressing existing gaps in service migration path planning studies， particularly the lack of predictive models for user mobility trajectories in urban scenarios and low similarity between planned and actual user paths， an algorithm was proposed for service migration path selection based on real-time user movement trajectories. The user’s future movement trajectory was predicted through a trajectory prediction algorithm based on Long Short-Term Memory （LSTM） model and a road network matching algorithm based on Hidden Markov Model （HMM）. Then， according to predicted movement trajectory and status information of nearby local base stations， the optimal migration edge server was selected， thereby completing service migration path selection in urban scenarios. On the dataset constructed from taxi trajectory dataset and mobile base station status dataset in Shenzhen， compared to the improved Depth-First Search （DFS） algorithm， improved A^* algorithm， Matrix-based Dynamic Multi-Path Selection （MDMPS） algorithm and Grid Division-based Service Migration Path Selection （GDSMPS） algorithm， the proposed algorithm reduced the average service migration time by 34.8%， 44.5%， 24.9% and 12.7% respectively， and increased average path similarity by 26.2%， 49.7%， 14.3% and 4.7% respectively. On noise datasets and long path datasets， the proposed algorithm had the smallest fluctuation in average service migration time and the highest average trajectory similarity. Experimental results show that the proposed algorithm not only effectively reduces service migration time， enhances the similarity between migration path and user movement path， but also has good resistance to data noise and excellent long-distance path planning capability.