Dual-motor-driven electric vehicles can realize independent torque distribution between front and rear, which in turn can obtain higher energy recovery efficiency. This paper proposes an improved and optimized braking energy recovery strategy based on the twin delayed deep deterministic (TD3) policy gradient algorithm for electric vehicles with dual motor drive. The strategy can maximize braking energy recovery while ensuring braking safety and comfort. First, an energy recovery decision-making framework based on deep reinforcement learning (DRL) is constructed, and a reward function that integrates the energy recovery effect, safety and comfort is designed. Then, the TD3 algorithm is used to solve this decision process, and an improved prioritized experience replay mechanism is proposed to accelerate the convergence speed of the strategy. Finally, the paper introduces a noise strategy for balanced exploration to enhance the algorithm's ability to explore and exploit. Validated by the Matlab/Simulink platform, the proposed algorithm is able to distribute the braking force more efficiently and effectively, improving the braking energy recovery efficiency under the premise of satisfying braking safety and comfort.