A primary motivation for analog integrated circuit (IC) design automation is the inefficiency of manual design in meeting increasingly stringent specifications, which often involve over 10 objectives. Recent advances in reinforcement learning (RL) emerge as a promising method, yet gaps remain when considering full design specifications, especially under process-voltage-temperature (PVT) variations. Excessive objectives lead to diminished reward signals, while varying PVT conditions result in conflicting gradients, both of which result in inefficient exploration. To address these, we propose a priority-based graph-enhanced RL framework. Specifically, using fuzzy logic converts quantitative rewards into qualitative priority signals, mitigating reward deterioration and enhancing exploration via entropy regularization. Furthermore, a graph-based representation compresses high-dimensional objective spaces under PVT variations into low-dimensional manifolds, enabling dynamic resource allocation to variation-sensitive regions and resolving gradient conflicts. Empirical results on various real-world analog ICs demonstrate that our method significantly outperforms existing RL algorithms, achieving superior solution quality and reducing simulation overhead.