This study presents an adaptive active disturbance rejection control strategy integrating deep reinforcement learning (DRL) with enhanced particle swarm optimization (PSO), aiming to improve the speed and thrust control performance of permanent magnet synchronous linear motors (PMSLMs). A mathematical model of the motor was established to analyze its dynamic characteristics, followed by the design of a DRL-PSO (DRLPSO) control framework. This framework leverages reward mechanisms in reinforcement learning to interact with the environment, dynamically optimizing ADRC parameters to accommodate varying operating conditions and external disturbances. The modified PSO algorithm incorporates partitioned inertia weights and cyclically utilizes historical global optimal data to iteratively update control policies, refining neural network weights and thereby enhancing search efficiency and optimization accuracy. Experimental results show that the proposed DRLPSO-ADRC method achieves significantly higher tracking precision in position and velocity, along with improved system stability and resistance to thrust disturbances, compared to conventional PSO-ADRC algorithms. These findings validate the effectiveness of the innovative control strategy.