With the increasing application of Connected and Autonomous Vehicle (CAV) technologies in active traffic management, Variable Speed Limit (VSL) strategies have become crucial for improving traffic flow efficiency and safety. To address the issues of decreased traffic capacity and abrupt speed variations caused by traffic conflicts in urban expressway merging areas, a cooperative variable speed limit (VSL) control strategy was proposed for the mainline and on-ramp under a connected vehicle environment. Firstly, a mainline traffic flow prediction model based on Motorway Traffic Flow Network Modle (METANET) was adopted, constructing a bi-objective function to minimize the total travel time and distance, using Model Predictive Control (MPC). Then, the variable speed limit control problem was modelled as a Markov decision process, with a composite reward function based on average speed, throughput, and vehicle delay. By introducing Deep Q-network (DQN), the optimal on-ramp speed limits under different traffic flow conditions were calculated and disseminated to CAVs through Vehicle-to-Infrastructure (V2I) communication. Finally, the proposed coordinated control strategy was simulated and tested using the North Third Ring Expressway in Xuzhou, China as a case study. The empirical results based on SUMO microsimulation demonstrate that the proposed strategy, compared to the scenario with speed control only on the mainline, reduces the total travel time of network vehicles by 8.51%, increases the average speed by 14.49%, and reduces traffic density fluctuations by 14.81%. These results demonstrate that the proposed method can effectively improve traffic flow efficiency in merging areas under a connected vehicle environment, reduce speed differences between mainline and ramp vehicles, and shrink the spatiotemporal scope of congestion, thereby enhancing the stability of urban expressway traffic flow.