This paper proposes a reinforcement learning-based multi-section, coordinated variable speed limit control approach for expressways. The method handles joint speed-limited decision-making at coupled spatiotemporal traffic dynamics across multiple sections and lanes. The traffic state space is constructed based on indicators: lane flow, speed, occupancy rate, number of slow-moving vehicles, and minimum Time-to-collision (TTC). Lane-level speed limits for each control unit are defined as action variables. A weighted reward function is designed to consider operational efficiency, safety, rationality of speed gradient, and control response. The model integrates Convolutional Neural Networks (CNNs) and Gated Recurrent Units (GRUs) to capture spatial correlations and time-varying properties in traffic-state information. In addition, a risk-aware dual-policy output mechanism is introduced to adaptively balance safety-oriented and efficiency-oriented control objectives. Using the Houjie-Xinlian section of the Guangshen Expressway as an example, three control strategies are simulated on the SUMO platform: CGDQN, FCDQN, and an uncontrolled scenario. Experimental results show that the total reward for CGDQN exceeds that of FCDQN. CGDQN reduces vehicle queueing and slow-moving traffic across various scenarios, thereby decreasing the likelihood of traffic accidents. The results demonstrate that the proposed method achieves coordinated multi-section variable speed limit control on expressways under different conditions, and is both feasible and effective.