To address the energy-efficient path planning problem of omnidirectional mobile robots in uncertain environments with multiple obstacles, this paper proposes an energy-aware path planning algorithm named energy-aware quadratic programming parameterization soft actor-critic(E-QPSAC), which integrates model predictive control parameterization with an improved soft actor-critic (SAC) framework. First, local path planning is formulated as a Quadratic Programming problem and efficiently solved by an improved primal-dual hybrid gradient algorithm. Second, a 12-dimensional state space (including robot pose, target information, and obstacle information) for SAC is constructed, along with a cascaded network architecture and a dynamic weight fusion mechanism to effectively balance obstacle avoidance and goal orientation. Meanwhile, considering the high energy consumption characteristics of omnidirectional mobile robots caused by environmental uncertainty, system nonlinearity, and motion characteristics, a five-dimensional energy consumption model is established, and an energy-saving reward function is designed based on this model. Finally, comparative experiments are conducted on a simulation platform with SAC, deep deterministic policy gradient (DDPG), and the prioritized replay and LSTM-based twin delayed deep deterministic policy gradient (PL-TD3) algorithm. The experimental results show that the proposed algorithm achieves a task completion rate of 98.7% (85.0% for SAC, 98.5% for DDPG, and 98.2% for PL-TD3), a total energy consumption as low as 18.274 kJ (25.765 kJ for SAC, 22.418 kJ for DDPG, and 21.191 kJ for PL-TD3), and an average path length of 6.45 m (6.56 m for SAC, 6.97 m for DDPG, and 7.13 m for PL-TD3). The proposed algorithm outperforms the others in task completion rate, energy consumption, path length, and robustness. The effectiveness of the proposed method is finally verified through practical experiments.