With the advancement of artificial intelligence technology and the progress in robot technology, the issue of path planning has garnered increasing attention. Reinforcement learning is extensively employed for goal-oriented path planning of mobile robots due to its data-independent nature and strong generalization capabilities. Despite some achievements, several challenges persist, including a scarcity of multi-objective path planning algorithms, low utilization rate of effective experiences, difficulties in model convergence, and sparse environment rewards. To address these issues, the Soft Actor-Critic (SAC) algorithm is applied to multi-objective path planning for the first time, and a flexible motion evaluation-based method that incorporates priority experience replay and expert experience is proposed. The proposed approach enhances sampling efficiency by prioritizing empirical playback while optimizing the reward function enables timely and rational feedback from the environment after each action to overcome local optima problems. Additionally, imitation learning based on expert experience improves training efficiency in reinforcement learning. Finally, simulations are conducted on the ROS platform for multi-objective path planning where results demonstrate that compared to the multi-objective SAC algorithm, the proposed algorithm accelerates convergence in both simple and complex environments with obstacles while generating shorter, smoother paths free from collisions.