This paper delves into the inherent graphtopological structure utilized by algorithms such as LandMark in the context of RFID indoor positioning. We uncover the essence of these algorithms, which leverage the implicit topological relationships within signal features for message passing and positioning accuracy. Despite the theoretical advantages, practical applications of the LandMark algorithm have been hindered by issues related to signal propagation, the limitations of topological structures, neighbor tag selection, and simplistic weight distribution methods. To address these limitations, we propose a series of innovative improvements. Our approach includes data preprocessing techniques like B-spline interpolation and normalization to mitigate environmental noise and enhance signal integrity. We introduce the concept of spatiotemporal graphs that map signals into a high-dimensional space, allowing for the construction of dynamic graph structures that more accurately capture the temporal dynamics of signals. Furthermore, we employ the PNAConv algorithm, a graph neural network technique, to refine the message passing and feature aggregation process, optimizing the selection of neighboring tags. Our experiments, conducted across various datasets, demonstrate that our model maintains low error rates, showcasing its high precision and robustness in diverse environments. The results not only validate the effectiveness of our improved algorithm but also highlight the importance of understanding and exploiting the graph-topological structure inherent in signal-based positioning systems.