The Java Platform Module System (JPMS) has found widespread applications since introduced in Java 9. However, existing pointer analyses fail to leverage the semantics of JPMS. This paper presents a novel module-aware approach to improving the soundness and performance of pointer analysis. For soundness, we model the semantics of keywords provides and uses in JPMS to recover missing points-to relations. For performance, we design a module-aware context-sensitive analysis, which can propagate and apply critical contexts (by exploiting modularity) to balance precision and efficiency better. We have implemented our module-aware pointer analysis named MPA in Tai-e and conducted extensive experiments to compare it with standard object-sensitive approaches. The evaluation results demonstrate that MPA improves soundness and enhances existing context-sensitivity, striking a good balance between efficiency and precision. In terms of soundness, MPA can increase the number of reachable methods up to 90.9 × 90.9× ( lombok lombok) under the same analysis. Performance-wise, MPA is nearly as fast as context-insensitivity for most benchmarks, while its precision is superior to that of 1-object-sensitivity on average.